{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hello, Colaboratory",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UjjwalSaxena/Automold--Road-Augmentation-Library/blob/master/optimalprice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "8K2t9M-6OLqb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import sklearn\n",
        "import sklearn.preprocessing as pp\n",
        "import sklearn.model_selection as ms\n",
        "import sklearn.metrics as metrics\n",
        "import json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yuG-eM74T9TP",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "outputId": "4c7f2e1e-5d3d-4fef-efbb-cfc0a8e65f85"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d7b85357-1b10-424d-9d29-892f9a3db0d7\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d7b85357-1b10-424d-9d29-892f9a3db0d7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving project_data.csv to project_data.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'project_data.csv': b'Geography,vendor,Risk,delivery_mix,Bid\\r\\nCollgCr,8,12,1,208500\\r\\nVeenker,1,12,2,181500\\r\\nCollgCr,8,12,1,223500\\r\\nCrawfor,9,12,4,140000\\r\\nNoRidge,8,12,2,250000\\r\\nMitchel,7,12,1,143000\\r\\nSomerst,1,12,1,307000\\r\\nNWAmes,8,12,4,200000\\r\\nOldTown,7,12,1,129900\\r\\nBrkSide,15,12,4,118000\\r\\nSawyer,1,12,1,129500\\r\\nNridgHt,8,14,1,345000\\r\\nSawyer,1,11,1,144000\\r\\nCollgCr,1,12,1,279500\\r\\nNAmes,1,11,4,157000\\r\\nBrkSide,4,15,4,132000\\r\\nNAmes,1,13,3,149000\\r\\nSawyer,11,9,1,90000\\r\\nSawyerW,1,10,1,159000\\r\\nNAmes,1,11,1,139000\\r\\nNridgHt,8,13,4,325300\\r\\nIDOTRR,4,14,1,139400\\r\\nCollgCr,1,13,1,230000\\r\\nMeadowV,12,12,1,129900\\r\\nSawyer,1,13,1,154000\\r\\nNridgHt,1,13,4,256300\\r\\nNAmes,1,12,4,134800\\r\\nNridgHt,1,13,1,306000\\r\\nNAmes,1,11,3,207500\\r\\nBrkSide,2,10,1,68500\\r\\nIDOTRR,9,8,1,40000\\r\\nSawyer,1,11,3,149350\\r\\nCollgCr,1,13,4,179900\\r\\nNAmes,1,10,1,165500\\r\\nNridgHt,12,14,1,277500\\r\\nNridgHt,8,13,1,309000\\r\\nCollgCr,1,10,4,145000\\r\\nNAmes,1,11,1,153000\\r\\nNAmes,1,12,1,109000\\r\\nEdwards,11,9,1,82000\\r\\nNAmes,1,11,1,160000\\r\\nTimber,1,11,1,170000\\r\\nSawyerW,10,12,3,144000\\r\\nCollgCr,1,11,3,130250\\r\\nNAmes,1,11,1,141000\\r\\nNridgHt,12,14,1,319900\\r\\nMitchel,7,12,3,239686\\r\\nSomerst,1,13,1,249700\\r\\nOldTown,15,9,1,113000\\r\\nSawyer,1,12,1,127000\\r\\nGilbert,8,12,4,177000\\r\\nBrkSide,7,12,1,114500\\r\\nIDOTRR,11,10,4,110000\\r\\nVeenker,1,14,1,385000\\r\\nNAmes,6,10,1,130000\\r\\nNAmes,1,11,1,180500\\r\\nSomerst,13,13,1,172500\\r\\nCollgCr,8,12,4,196500\\r\\nStoneBr,8,15,3,438780\\r\\nCollgCr,1,12,1,124900\\r\\nSawyerW,1,11,1,158000\\r\\nIDOTRR,5,12,1,101000\\r\\nNridgHt,12,13,1,202500\\r\\nOldTown,9,13,1,140000\\r\\nCollgCr,8,12,1,219500\\r\\nNridgHt,8,13,1,317000\\r\\nNAmes,1,12,1,180000\\r\\nCollgCr,1,12,1,226000\\r\\nOldTown,2,10,4,80000\\r\\nClearCr,7,11,4,225000\\r\\nNAmes,1,13,1,244000\\r\\nMitchel,1,10,4,129500\\r\\nGilbert,8,12,4,185000\\r\\nNAmes,1,12,1,144900\\r\\nOldTown,7,9,4,107400\\r\\nMeadowV,14,9,1,91000\\r\\nNAmes,1,11,1,135750\\r\\nBrkSide,7,10,1,127000\\r\\nSawyer,11,9,1,136500\\r\\nOldTown,7,11,4,110000\\r\\nNAmes,8,12,4,193500\\r\\nMitchel,12,11,2,153500\\r\\nSomerst,1,13,1,245000\\r\\nNAmes,1,10,1,126500\\r\\nGilbert,6,12,1,168500\\r\\nNoRidge,8,13,4,260000\\r\\nGilbert,8,11,1,174000\\r\\nSomerst,13,11,4,164500\\r\\nIDOTRR,7,5,4,85000\\r\\nCollgCr,1,9,1,123600\\r\\nNAmes,1,9,1,109900\\r\\nNAmes,1,8,1,98600\\r\\nCrawfor,2,12,1,163500\\r\\nOldTown,15,12,4,133900\\r\\nCollgCr,8,11,1,204750\\r\\nGilbert,8,14,4,185000\\r\\nCollgCr,1,12,1,214000\\r\\nEdwards,1,9,1,94750\\r\\nEdwards,2,10,4,83000\\r\\nNAmes,1,9,1,128950\\r\\nNWAmes,1,13,1,205000\\r\\nSawyerW,8,11,1,178000\\r\\nSawyerW,11,10,1,118964\\r\\nCollgCr,1,12,4,198900\\r\\nIDOTRR,7,11,4,169500\\r\\nSomerst,8,13,1,250000\\r\\nOldTown,2,11,1,100000\\r\\nOldTown,1,10,1,115000\\r\\nIDOTRR,7,12,4,115000\\r\\nNWAmes,1,12,1,190000\\r\\nEdwards,7,10,1,136900\\r\\nGilbert,6,12,1,180000\\r\\nCollgCr,8,12,1,383970\\r\\nCrawfor,1,11,4,217000\\r\\nCrawfor,9,14,1,259500\\r\\nSomerst,13,11,4,176000\\r\\nSawyer,1,10,1,139000\\r\\nEdwards,1,10,4,155000\\r\\nSawyerW,8,12,4,320000\\r\\nCollgCr,8,11,1,163990\\r\\nClearCr,6,11,3,180000\\r\\nIDOTRR,7,9,1,100000\\r\\nNAmes,1,13,4,136000\\r\\nSawyerW,12,11,1,153900\\r\\nNWAmes,1,11,3,181000\\r\\nIDOTRR,15,14,1,84500\\r\\nNPkVill,12,11,1,128000\\r\\nOldTown,4,12,1,87000\\r\\nNAmes,8,11,1,155000\\r\\nNAmes,1,12,1,150000\\r\\nNAmes,8,13,4,226000\\r\\nGilbert,8,11,4,244000\\r\\nNAmes,1,11,4,150750\\r\\nTimber,1,13,1,220000\\r\\nSawyer,1,11,1,180000\\r\\nNWAmes,1,13,1,174000\\r\\nNAmes,1,10,4,143000\\r\\nMitchel,11,12,1,171000\\r\\nCollgCr,8,13,1,230000\\r\\nCollgCr,8,11,1,231500\\r\\nNAmes,1,9,2,115000\\r\\nCollgCr,1,12,1,260000\\r\\nNAmes,7,9,4,166000\\r\\nCollgCr,1,12,1,204000\\r\\nSawyer,11,10,1,125000\\r\\nEdwards,13,11,1,130000\\r\\nBrkSide,2,12,4,105000\\r\\nGilbert,8,12,3,222500\\r\\nSawyerW,1,12,1,141000\\r\\nBrkSide,7,9,1,115000\\r\\nCollgCr,1,11,4,122000\\r\\nNridgHt,1,13,1,372402\\r\\nNWAmes,8,11,3,190000\\r\\nClearCr,1,13,1,235000\\r\\nOldTown,2,11,4,125000\\r\\nEdwards,7,11,4,79000\\r\\nNAmes,1,12,1,109500\\r\\nTimber,8,13,4,269500\\r\\nSomerst,8,12,4,254900\\r\\nGilbert,8,12,4,320000\\r\\nVeenker,1,12,3,162500\\r\\nNridgHt,8,14,1,412500\\r\\nNridgHt,1,12,4,220000\\r\\nOldTown,4,10,1,103200\\r\\nOldTown,3,13,4,152000\\r\\nEdwards,15,12,1,127500\\r\\nClearCr,1,10,1,190000\\r\\nNridgHt,8,13,1,325624\\r\\nGilbert,8,12,1,183500\\r\\nTimber,1,14,4,228000\\r\\nOldTown,7,11,1,128500\\r\\nNAmes,1,11,4,215000\\r\\nStoneBr,13,14,1,239000\\r\\nNAmes,1,11,1,163000\\r\\nTimber,1,11,1,184000\\r\\nEdwards,1,13,4,243000\\r\\nClearCr,8,11,4,211000\\r\\nSawyer,7,10,1,172500\\r\\nStoneBr,1,14,3,501837\\r\\nOldTown,2,11,1,100000\\r\\nSomerst,13,11,1,177000\\r\\nCrawfor,9,13,1,200100\\r\\nEdwards,1,11,1,120000\\r\\nOldTown,7,12,1,200000\\r\\nBrkSide,7,13,1,127000\\r\\nOldTown,5,19,1,475000\\r\\nMitchel,6,12,3,173000\\r\\nOldTown,7,12,1,135000\\r\\nSawyerW,11,10,1,153337\\r\\nStoneBr,12,13,1,286000\\r\\nCrawfor,9,16,1,315000\\r\\nNAmes,8,16,3,184000\\r\\nCollgCr,1,12,1,192000\\r\\nEdwards,13,12,1,130000\\r\\nCollgCr,1,12,1,127000\\r\\nNPkVill,13,12,2,148500\\r\\nSomerst,1,12,1,311872\\r\\nNAmes,5,12,4,235000\\r\\nOldTown,5,12,4,104000\\r\\nNridgHt,1,13,1,274900\\r\\nEdwards,1,9,4,140000\\r\\nMitchel,1,12,1,171500\\r\\nOldTown,7,12,4,112000\\r\\nCollgCr,12,11,1,149000\\r\\nOldTown,7,12,1,110000\\r\\nGilbert,1,12,4,180500\\r\\nSawyer,1,10,3,143900\\r\\nNAmes,1,9,1,141000\\r\\nSawyerW,8,12,1,277000\\r\\nNAmes,1,13,1,145000\\r\\nEdwards,2,11,1,98000\\r\\nEdwards,1,11,4,186000\\r\\nSomerst,8,12,1,252678\\r\\nCollgCr,1,10,3,156000\\r\\nCollgCr,8,13,2,161750\\r\\nNAmes,1,11,1,134450\\r\\nCollgCr,1,12,1,210000\\r\\nOldTown,9,8,1,107000\\r\\nCrawfor,7,16,4,311500\\r\\nBlmngtn,12,12,1,167240\\r\\nCollgCr,1,12,1,204900\\r\\nGilbert,8,11,1,200000\\r\\nNWAmes,8,12,1,179900\\r\\nNAmes,1,10,2,97000\\r\\nNridgHt,1,15,1,386250\\r\\nBrDale,13,10,1,112000\\r\\nNoRidge,8,12,1,290000\\r\\nBrDale,13,12,1,106000\\r\\nSawyer,1,10,2,125000\\r\\nBlmngtn,12,12,1,192500\\r\\nNAmes,1,12,1,148000\\r\\nNoRidge,8,13,1,403000\\r\\nBrDale,13,11,1,94500\\r\\nCollgCr,1,11,4,128200\\r\\nGilbert,8,11,1,216500\\r\\nBrDale,13,9,1,89500\\r\\nCollgCr,1,12,2,185500\\r\\nSawyerW,8,14,3,194500\\r\\nNridgHt,1,13,1,318000\\r\\nEdwards,7,10,1,113000\\r\\nSomerst,1,13,1,262500\\r\\nOldTown,2,14,1,110500\\r\\nOldTown,7,9,4,79000\\r\\nSawyerW,13,12,4,120000\\r\\nSawyerW,8,12,1,205000\\r\\nNWAmes,1,12,1,241500\\r\\nOldTown,15,14,1,137000\\r\\nNAmes,1,11,1,140000\\r\\nCollgCr,8,12,1,180000\\r\\nClearCr,7,13,3,277000\\r\\nBrkSide,2,5,1,76500\\r\\nCrawfor,12,13,1,235000\\r\\nSawyerW,8,11,1,173000\\r\\nNAmes,6,13,1,158000\\r\\nNAmes,1,11,1,145000\\r\\nGilbert,8,12,1,230000\\r\\nSomerst,8,11,1,207500\\r\\nCollgCr,1,12,1,220000\\r\\nCollgCr,8,12,1,231500\\r\\nOldTown,1,10,1,97000\\r\\nNAmes,6,11,4,176000\\r\\nCollgCr,8,13,1,276000\\r\\nSawyer,6,13,4,151000\\r\\nOldTown,7,12,4,130000\\r\\nOldTown,2,10,1,73000\\r\\nNWAmes,1,12,1,175500\\r\\nGilbert,8,11,2,185000\\r\\nSWISU,5,13,1,179500\\r\\nIDOTRR,2,11,1,120500\\r\\nEdwards,1,13,4,148000\\r\\nSomerst,8,13,1,266000\\r\\nClearCr,1,14,3,241500\\r\\nNoRidge,8,15,3,290000\\r\\nNAmes,1,12,1,139000\\r\\nMitchel,1,12,4,124500\\r\\nBrkSide,7,14,1,205000\\r\\nMitchel,1,12,1,201000\\r\\nGilbert,1,9,4,141000\\r\\nNridgHt,1,14,1,415298\\r\\nClearCr,8,12,1,192000\\r\\nSawyerW,8,13,1,228500\\r\\nSomerst,1,11,1,185000\\r\\nNridgHt,12,12,1,207500\\r\\nSomerst,1,13,1,244600\\r\\nSawyerW,12,11,1,179200\\r\\nSomerst,13,12,1,164700\\r\\nNAmes,7,13,1,159000\\r\\nNAmes,1,8,4,88000\\r\\nSawyer,1,10,1,122000\\r\\nBrkSide,9,13,1,153575\\r\\nCollgCr,8,13,1,233230\\r\\nSWISU,15,11,1,135900\\r\\nEdwards,7,9,1,131000\\r\\nNWAmes,8,14,4,235000\\r\\nNAmes,1,11,1,167000\\r\\nMitchel,6,12,3,142500\\r\\nIDOTRR,7,10,1,152000\\r\\nSomerst,8,12,1,239000\\r\\nNWAmes,8,12,1,175000\\r\\nCrawfor,1,14,1,158500\\r\\nCrawfor,15,10,4,157000\\r\\nCollgCr,8,13,1,267000\\r\\nCollgCr,1,12,4,205000\\r\\nCollgCr,1,12,4,149900\\r\\nOldTown,5,16,1,295000\\r\\nCollgCr,1,13,1,305900\\r\\nSawyerW,8,12,1,225000\\r\\nIDOTRR,7,13,1,89500\\r\\nEdwards,2,9,1,82500\\r\\nNridgHt,1,14,1,360000\\r\\nGilbert,8,11,1,165600\\r\\nNAmes,1,12,1,132000\\r\\nOldTown,15,12,1,119900\\r\\nTimber,1,12,1,375000\\r\\nOldTown,9,14,1,178000\\r\\nGilbert,8,12,1,188500\\r\\nNWAmes,8,14,4,260000\\r\\nSomerst,8,13,1,270000\\r\\nNoRidge,8,12,1,260000\\r\\nNWAmes,6,12,1,187500\\r\\nNridgHt,8,14,4,342643\\r\\nNridgHt,8,13,1,354000\\r\\nSawyerW,8,12,1,301000\\r\\nOldTown,1,11,1,126175\\r\\nNAmes,6,14,4,242000\\r\\nIDOTRR,4,11,1,87000\\r\\nVeenker,12,13,3,324000\\r\\nNAmes,1,11,1,145250\\r\\nBrkSide,5,12,1,214500\\r\\nIDOTRR,9,10,4,78000\\r\\nNAmes,11,9,1,119000\\r\\nNAmes,1,11,1,139000\\r\\nNridgHt,1,13,1,284000\\r\\nNridgHt,12,12,5,207000\\r\\nGilbert,8,11,1,192000\\r\\nTimber,15,11,4,228950\\r\\nStoneBr,1,14,4,377426\\r\\nCollgCr,1,12,1,214000\\r\\nNWAmes,1,14,4,202500\\r\\nNAmes,1,13,1,155000\\r\\nTimber,8,13,1,202900\\r\\nSawyerW,1,8,1,82000\\r\\nNAmes,11,7,1,87500\\r\\nNridgHt,12,14,1,266000\\r\\nMeadowV,13,8,1,85000\\r\\nBrkSide,7,11,1,140200\\r\\nNAmes,1,14,3,151500\\r\\nNAmes,1,11,1,157500\\r\\nNridgHt,13,12,1,154000\\r\\nNridgHt,8,14,1,437154\\r\\nNridgHt,12,14,1,318061\\r\\nClearCr,12,12,1,190000\\r\\nEdwards,7,11,1,95000\\r\\nOldTown,2,14,1,105900\\r\\nSWISU,7,11,1,140000\\r\\nCollgCr,1,11,1,177500\\r\\nGilbert,1,12,1,173000\\r\\nMeadowV,12,10,1,134000\\r\\nClearCr,6,9,1,130000\\r\\nNoRidge,8,13,3,280000\\r\\nMitchel,10,12,3,156000\\r\\nBrkSide,7,10,1,145000\\r\\nEdwards,10,12,4,198500\\r\\nBrDale,13,14,1,118000\\r\\nNWAmes,8,11,2,190000\\r\\nIDOTRR,9,12,1,147000\\r\\nNAmes,1,11,1,159000\\r\\nNAmes,6,11,4,165000\\r\\nNAmes,1,11,1,132000\\r\\nNAmes,1,12,4,162000\\r\\nGilbert,8,11,1,172400\\r\\nClearCr,7,8,1,134432\\r\\nSawyerW,12,11,1,125000\\r\\nNAmes,1,11,1,123000\\r\\nCollgCr,8,12,1,219500\\r\\nEdwards,2,2,1,61000\\r\\nCollgCr,10,10,3,148000\\r\\nSomerst,8,13,4,340000\\r\\nStoneBr,1,11,4,394432\\r\\nGilbert,8,11,1,179000\\r\\nSWISU,7,11,1,127000\\r\\nSomerst,1,12,1,187750\\r\\nCollgCr,8,12,1,213500\\r\\nSawyerW,4,9,4,76000\\r\\nClearCr,8,11,4,240000\\r\\nBlmngtn,12,13,1,192000\\r\\nEdwards,7,8,2,81000\\r\\nEdwards,6,12,1,125000\\r\\nCollgCr,1,12,3,191000\\r\\nNridgHt,8,15,1,426000\\r\\nEdwards,7,13,1,119000\\r\\nMitchel,8,11,3,215000\\r\\nNAmes,1,12,1,106500\\r\\nBrkSide,2,9,4,100000\\r\\nOldTown,7,11,1,109000\\r\\nEdwards,1,11,1,129000\\r\\nCollgCr,1,10,1,123000\\r\\nNAmes,8,10,1,169500\\r\\nIDOTRR,2,7,4,67000\\r\\nSomerst,8,12,1,241000\\r\\nVeenker,12,13,1,245500\\r\\nCollgCr,1,12,1,164990\\r\\nSawyer,2,13,1,108000\\r\\nNoRidge,8,13,4,258000\\r\\nGilbert,8,11,1,168000\\r\\nSawyer,1,8,4,150000\\r\\nSWISU,7,11,1,115000\\r\\nCrawfor,9,13,1,177000\\r\\nNridgHt,8,12,4,280000\\r\\nSomerst,8,13,1,339750\\r\\nEdwards,1,8,1,60000\\r\\nGilbert,15,10,1,145000\\r\\nSomerst,1,12,1,222000\\r\\nOldTown,2,11,1,115000\\r\\nSawyerW,8,12,3,228000\\r\\nGilbert,1,12,1,181134\\r\\nSawyer,8,13,1,149500\\r\\nCrawfor,9,12,1,239000\\r\\nEdwards,7,11,1,126000\\r\\nNAmes,1,11,1,142000\\r\\nMitchel,11,12,1,206300\\r\\nNWAmes,1,13,2,215000\\r\\nMitchel,1,10,1,113000\\r\\nNoRidge,8,13,1,315000\\r\\nNAmes,1,11,1,139000\\r\\nOldTown,8,15,1,135000\\r\\nSawyerW,6,12,1,275000\\r\\nNAmes,1,10,1,109008\\r\\nCollgCr,1,12,1,195400\\r\\nTimber,1,11,4,175000\\r\\nBrDale,13,11,1,85400\\r\\nOldTown,7,13,1,79900\\r\\nBrDale,13,10,1,122500\\r\\nGilbert,8,11,4,181000\\r\\nMeadowV,14,11,1,81000\\r\\nCollgCr,8,13,3,212000\\r\\nOldTown,7,14,1,116000\\r\\nBrkSide,4,13,1,119000\\r\\nCrawfor,2,11,1,90350\\r\\nEdwards,7,14,4,110000\\r\\nNridgHt,1,15,1,555000\\r\\nEdwards,11,8,1,118000\\r\\nBrkSide,7,12,1,162900\\r\\nBlmngtn,12,12,1,172500\\r\\nCollgCr,8,12,1,210000\\r\\nEdwards,1,11,4,127500\\r\\nNAmes,1,12,4,190000\\r\\nGilbert,8,12,4,199900\\r\\nIDOTRR,7,12,1,119500\\r\\nOldTown,7,10,1,120000\\r\\nOldTown,2,14,1,110000\\r\\nClearCr,1,12,1,280000\\r\\nTimber,8,11,4,204000\\r\\nSomerst,8,13,1,210000\\r\\nMitchel,11,10,1,188000\\r\\nNWAmes,1,13,1,175500\\r\\nOldTown,9,10,1,98000\\r\\nClearCr,1,10,3,256000\\r\\nOldTown,9,15,1,161000\\r\\nBrkSide,7,9,4,110000\\r\\nSomerst,8,13,1,263435\\r\\nSWISU,9,16,1,155000\\r\\nSawyer,1,10,1,62383\\r\\nCrawfor,9,13,1,188700\\r\\nCollgCr,1,10,1,124000\\r\\nBlmngtn,12,12,1,178740\\r\\nNAmes,1,12,1,167000\\r\\nNAmes,9,12,1,146500\\r\\nNridgHt,1,13,1,250000\\r\\nSawyerW,8,11,4,187000\\r\\nStoneBr,12,13,4,212000\\r\\nNWAmes,8,13,1,190000\\r\\nEdwards,14,11,1,148000\\r\\nNridgHt,1,13,1,440000\\r\\nStoneBr,12,13,1,251000\\r\\nSawyer,1,11,4,132500\\r\\nCollgCr,1,11,1,208900\\r\\nNridgHt,8,14,1,380000\\r\\nCollgCr,1,13,1,297000\\r\\nOldTown,2,11,1,89471\\r\\nNridgHt,1,14,2,326000\\r\\nNridgHt,1,14,1,374000\\r\\nOldTown,9,15,4,155000\\r\\nMitchel,12,11,2,164000\\r\\nSawyer,1,12,4,132500\\r\\nNAmes,1,12,1,147000\\r\\nNAmes,1,12,1,156000\\r\\nNWAmes,1,11,1,175000\\r\\nOldTown,15,9,4,160000\\r\\nMeadowV,14,12,1,86000\\r\\nMeadowV,13,11,1,115000\\r\\nNAmes,7,13,1,133000\\r\\nGilbert,8,11,4,172785\\r\\nNAmes,1,11,1,155000\\r\\nOldTown,2,13,1,91300\\r\\nIDOTRR,2,9,1,34900\\r\\nNoRidge,1,13,1,430000\\r\\nBrkSide,7,13,1,184000\\r\\nSawyer,1,12,1,130000\\r\\nNAmes,1,12,1,120000\\r\\nBrDale,13,11,1,113000\\r\\nSomerst,8,12,1,226700\\r\\nEdwards,1,12,4,140000\\r\\nCrawfor,1,15,1,289000\\r\\nNPkVill,13,11,1,147000\\r\\nOldTown,11,10,1,124500\\r\\nSawyerW,8,13,1,215000\\r\\nSomerst,1,11,1,208300\\r\\nOldTown,9,16,1,161000\\r\\nNAmes,1,11,4,124500\\r\\nNAmes,1,12,1,164900\\r\\nNridgHt,12,12,1,202665\\r\\nNAmes,1,10,4,129900\\r\\nMitchel,1,11,4,134000\\r\\nCrawfor,4,10,1,96500\\r\\nNridgHt,1,15,1,402861\\r\\nNWAmes,6,12,4,158000\\r\\nNoRidge,8,12,1,265000\\r\\nCollgCr,8,11,3,211000\\r\\nCrawfor,9,16,1,234000\\r\\nOldTown,15,11,1,106250\\r\\nNAmes,1,12,4,150000\\r\\nBrkSide,7,13,4,159000\\r\\nEdwards,8,15,1,184750\\r\\nNoRidge,8,12,1,315750\\r\\nSomerst,1,12,1,176000\\r\\nNAmes,1,12,1,132000\\r\\nNridgHt,8,14,1,446261\\r\\nEdwards,2,11,1,86000\\r\\nCrawfor,1,9,3,200624\\r\\nTimber,6,11,1,175000\\r\\nBrkSide,9,14,5,128000\\r\\nNAmes,1,12,1,107500\\r\\nBrkSide,1,4,1,39300\\r\\nGilbert,8,13,1,178000\\r\\nEdwards,15,12,1,107500\\r\\nCollgCr,8,12,3,188000\\r\\nNAmes,1,9,2,111250\\r\\nSawyer,1,10,1,158000\\r\\nCollgCr,1,13,1,272000\\r\\nTimber,1,14,1,315000\\r\\nNoRidge,8,13,2,248000\\r\\nNWAmes,1,12,1,213250\\r\\nNAmes,12,12,1,133000\\r\\nGilbert,8,12,1,179665\\r\\nNWAmes,7,12,4,229000\\r\\nBrkSide,7,13,1,210000\\r\\nMitchel,10,12,1,129500\\r\\nOldTown,1,12,1,125000\\r\\nSomerst,8,12,1,263000\\r\\nNPkVill,12,12,1,140000\\r\\nOldTown,1,11,1,112500\\r\\nNridgHt,1,13,1,255500\\r\\nEdwards,1,9,1,108000\\r\\nNridgHt,8,12,1,284000\\r\\nBrkSide,4,11,1,113000\\r\\nNAmes,1,10,1,141000\\r\\nIDOTRR,7,10,1,108000\\r\\nGilbert,8,12,2,175000\\r\\nBlmngtn,12,12,1,234000\\r\\nSawyer,1,11,1,121500\\r\\nMitchel,1,10,1,170000\\r\\nEdwards,2,11,1,108000\\r\\nEdwards,7,13,1,185000\\r\\nNoRidge,8,12,3,268000\\r\\nSWISU,9,10,4,128000\\r\\nStoneBr,8,14,1,325000\\r\\nNridgHt,1,12,1,214000\\r\\nSawyerW,7,16,1,316600\\r\\nNAmes,11,10,4,135960\\r\\nNAmes,11,10,1,142600\\r\\nNAmes,1,12,1,120000\\r\\nTimber,8,12,4,224500\\r\\nGilbert,6,12,1,170000\\r\\nNAmes,6,12,2,139000\\r\\nNAmes,7,10,1,118500\\r\\nSWISU,7,14,1,145000\\r\\nSawyer,6,11,1,164500\\r\\nSomerst,13,12,4,146000\\r\\nOldTown,7,10,1,131500\\r\\nNAmes,1,12,3,181900\\r\\nNridgHt,1,13,1,253293\\r\\nEdwards,11,11,1,118500\\r\\nOldTown,5,19,1,325000\\r\\nBrkSide,7,11,1,133000\\r\\nTimber,1,13,1,369900\\r\\nBrkSide,2,13,1,130000\\r\\nSawyer,10,11,1,137000\\r\\nClearCr,1,13,1,143000\\r\\nBrkSide,3,11,1,79500\\r\\nCollgCr,8,12,1,185900\\r\\nNridgHt,8,15,4,451950\\r\\nMitchel,1,13,1,138000\\r\\nCollgCr,12,11,1,140000\\r\\nCollgCr,1,11,1,110000\\r\\nStoneBr,1,13,1,319000\\r\\nOldTown,9,13,1,114504\\r\\nBlmngtn,12,12,1,194201\\r\\nCrawfor,1,11,1,217500\\r\\nBlueste,13,12,1,151000\\r\\nNridgHt,8,13,1,275000\\r\\nIDOTRR,7,12,1,141000\\r\\nSawyerW,8,13,1,220000\\r\\nSomerst,13,12,1,151000\\r\\nCollgCr,1,12,1,221000\\r\\nNAmes,8,13,1,205000\\r\\nCollgCr,1,10,4,152000\\r\\nEdwards,1,13,1,225000\\r\\nCrawfor,9,14,1,359100\\r\\nSawyer,1,9,1,118500\\r\\nCollgCr,8,14,1,313000\\r\\nNWAmes,6,12,2,148000\\r\\nCollgCr,8,13,1,261500\\r\\nMitchel,1,10,1,147000\\r\\nMeadowV,14,10,1,75500\\r\\nNAmes,10,13,1,137500\\r\\nGilbert,8,11,1,183200\\r\\nNAmes,4,12,4,105500\\r\\nNridgHt,1,14,1,314813\\r\\nTimber,8,13,1,305000\\r\\nEdwards,2,6,1,67000\\r\\nNWAmes,8,13,1,240000\\r\\nSawyer,1,11,1,135000\\r\\nSomerst,13,11,1,168500\\r\\nNWAmes,8,11,1,165150\\r\\nNAmes,1,12,4,160000\\r\\nNAmes,1,10,1,139900\\r\\nNAmes,6,12,1,153000\\r\\nNAmes,8,10,1,135000\\r\\nNAmes,6,11,4,168500\\r\\nOldTown,9,11,4,124000\\r\\nNridgHt,12,13,1,209500\\r\\nNWAmes,1,12,1,82500\\r\\nNAmes,1,12,4,139400\\r\\nOldTown,11,11,1,144000\\r\\nSWISU,15,13,1,200000\\r\\nBrkSide,2,5,1,60000\\r\\nOldTown,15,9,1,93000\\r\\nEdwards,2,12,1,85000\\r\\nBlmngtn,12,13,1,264561\\r\\nNridgHt,12,13,1,274000\\r\\nSomerst,8,12,1,226000\\r\\nNAmes,6,15,1,345000\\r\\nNWAmes,8,10,1,152000\\r\\nSomerst,1,14,1,370878\\r\\nNAmes,1,11,4,143250\\r\\nNAmes,1,10,1,98300\\r\\nEdwards,1,11,1,155000\\r\\nNAmes,8,11,1,155000\\r\\nMeadowV,14,10,1,84500\\r\\nSomerst,8,13,1,205950\\r\\nEdwards,9,9,1,108000\\r\\nCollgCr,8,12,1,191000\\r\\nIDOTRR,7,13,1,135000\\r\\nNoRidge,1,14,1,350000\\r\\nBrDale,13,11,1,88000\\r\\nNAmes,1,12,1,145500\\r\\nCrawfor,9,13,1,149000\\r\\nNAmes,7,11,1,97500\\r\\nEdwards,1,12,4,167000\\r\\nNWAmes,8,14,3,197900\\r\\nNoRidge,8,15,3,402000\\r\\nNAmes,1,9,4,110000\\r\\nEdwards,10,9,1,137500\\r\\nSomerst,1,13,3,423000\\r\\nGilbert,8,13,4,230500\\r\\nNAmes,8,11,1,129000\\r\\nSawyerW,1,11,1,193500\\r\\nSawyer,1,11,4,168000\\r\\nCrawfor,2,9,1,137500\\r\\nCollgCr,8,11,2,173500\\r\\nEdwards,9,12,1,103600\\r\\nVeenker,1,12,1,165000\\r\\nCrawfor,1,13,1,257500\\r\\nNAmes,1,12,1,140000\\r\\nNPkVill,13,12,1,148500\\r\\nOldTown,9,6,1,87000\\r\\nOldTown,2,13,1,109500\\r\\nStoneBr,1,13,1,372500\\r\\nSawyer,1,10,1,128500\\r\\nSawyerW,12,11,1,143000\\r\\nSWISU,7,10,1,159434\\r\\nClearCr,12,11,1,173000\\r\\nCollgCr,1,14,4,285000\\r\\nNoRidge,8,12,3,221000\\r\\nStoneBr,13,12,3,207500\\r\\nSomerst,8,13,1,227875\\r\\nSomerst,13,12,2,148800\\r\\nStoneBr,1,14,1,392000\\r\\nNridgHt,12,11,4,194700\\r\\nCollgCr,12,11,1,141000\\r\\nNoRidge,8,16,4,755000\\r\\nTimber,8,12,1,335000\\r\\nOldTown,2,11,4,108480\\r\\nBrkSide,7,11,4,141500\\r\\nTimber,1,12,1,176000\\r\\nBrkSide,2,12,1,89000\\r\\nEdwards,1,12,1,123500\\r\\nSawyer,1,13,1,138500\\r\\nSomerst,12,12,1,196000\\r\\nTimber,1,13,1,312500\\r\\nNWAmes,1,12,1,140000\\r\\nStoneBr,8,13,1,361919\\r\\nOldTown,15,14,1,140000\\r\\nCollgCr,1,12,1,213000\\r\\nIDOTRR,15,9,1,55000\\r\\nClearCr,1,12,3,302000\\r\\nNridgHt,12,13,1,254000\\r\\nGilbert,8,12,1,179540\\r\\nSawyer,1,12,1,109900\\r\\nBrkSide,2,9,1,52000\\r\\nIDOTRR,7,11,1,102776\\r\\nStoneBr,12,13,1,189000\\r\\nBrkSide,15,9,1,129000\\r\\nSawyer,8,14,3,130500\\r\\nNWAmes,1,11,1,165000\\r\\nOldTown,9,15,1,159500\\r\\nNWAmes,1,11,1,157000\\r\\nNoRidge,8,12,1,341000\\r\\nCollgCr,1,11,1,128500\\r\\nStoneBr,12,13,3,275000\\r\\nCollgCr,12,11,1,143000\\r\\nNAmes,1,11,1,124500\\r\\nEdwards,7,10,1,135000\\r\\nEdwards,1,14,1,320000\\r\\nNAmes,1,10,1,120500\\r\\nCrawfor,1,15,4,222000\\r\\nCollgCr,1,12,1,194500\\r\\nNAmes,11,10,4,110000\\r\\nIDOTRR,2,9,1,103000\\r\\nStoneBr,12,13,1,236500\\r\\nTimber,6,12,4,187500\\r\\nCollgCr,8,12,1,222500\\r\\nSawyer,1,11,4,131400\\r\\nSawyer,1,10,4,108000\\r\\nOldTown,5,14,1,163000\\r\\nNAmes,11,7,1,93500\\r\\nGilbert,8,13,3,239900\\r\\nEdwards,11,10,1,179000\\r\\nCollgCr,8,12,1,190000\\r\\nOldTown,9,12,1,132000\\r\\nSawyer,1,14,1,142000\\r\\nSawyerW,1,12,1,179000\\r\\nSawyer,6,11,1,175000\\r\\nStoneBr,12,13,1,180000\\r\\nNWAmes,8,17,1,299800\\r\\nGilbert,8,12,1,236000\\r\\nOldTown,9,14,4,265979\\r\\nNoRidge,1,12,1,260400\\r\\nEdwards,7,7,1,98000\\r\\nOldTown,7,11,4,96500\\r\\nGilbert,8,12,1,162000\\r\\nCollgCr,1,11,1,217000\\r\\nNridgHt,8,13,1,275500\\r\\nNAmes,1,14,1,156000\\r\\nSomerst,13,11,1,172500\\r\\nCollgCr,8,13,1,212000\\r\\nSawyer,8,11,3,158900\\r\\nSomerst,13,12,1,179400\\r\\nNoRidge,8,13,1,290000\\r\\nNAmes,1,12,1,127500\\r\\nBrkSide,2,10,2,100000\\r\\nSomerst,8,12,1,215200\\r\\nNoRidge,8,13,1,337000\\r\\nVeenker,12,13,3,270000\\r\\nSomerst,1,14,1,264132\\r\\nNWAmes,8,12,1,196500\\r\\nMitchel,7,13,1,160000\\r\\nCollgCr,1,12,1,216837\\r\\nStoneBr,8,13,3,538000\\r\\nSawyer,10,10,3,134900\\r\\nEdwards,1,9,1,102000\\r\\nEdwards,6,11,1,107000\\r\\nNAmes,1,10,1,114500\\r\\nNridgHt,1,13,4,395000\\r\\nMitchel,12,11,2,162000\\r\\nCollgCr,1,12,1,221500\\r\\nSawyer,1,10,1,142500\\r\\nSawyerW,11,10,1,144000\\r\\nMitchel,11,11,1,135000\\r\\nGilbert,1,12,1,176000\\r\\nSawyerW,8,11,1,175900\\r\\nCollgCr,1,12,1,187100\\r\\nMitchel,10,11,4,165500\\r\\nOldTown,5,12,1,128000\\r\\nNWAmes,1,11,1,161500\\r\\nOldTown,7,11,1,139000\\r\\nSawyerW,8,12,1,233000\\r\\nOldTown,1,11,4,107900\\r\\nClearCr,8,14,1,187500\\r\\nBlmngtn,12,12,1,160200\\r\\nMitchel,6,11,4,146800\\r\\nNoRidge,8,12,3,269790\\r\\nSomerst,1,13,1,225000\\r\\nGilbert,8,12,4,194500\\r\\nSawyerW,8,12,1,171000\\r\\nSawyer,1,11,1,143500\\r\\nNAmes,1,10,1,110000\\r\\nNridgHt,8,14,1,485000\\r\\nSWISU,7,12,4,175000\\r\\nClearCr,8,11,1,200000\\r\\nIDOTRR,2,11,1,109900\\r\\nCollgCr,8,12,1,189000\\r\\nNridgHt,8,14,1,582933\\r\\nNAmes,1,10,1,118000\\r\\nSomerst,1,12,2,227680\\r\\nNAmes,6,10,1,135500\\r\\nBrkSide,9,11,1,223500\\r\\nNAmes,6,10,1,159950\\r\\nOldTown,5,10,4,106000\\r\\nNWAmes,1,12,1,181000\\r\\nCollgCr,12,11,1,144500\\r\\nIDOTRR,1,10,1,55993\\r\\nNAmes,1,12,1,157900\\r\\nEdwards,7,12,1,116000\\r\\nCollgCr,1,12,3,224900\\r\\nNAmes,1,11,4,137000\\r\\nMitchel,1,13,3,271000\\r\\nClearCr,6,13,1,155000\\r\\nNridgHt,12,12,1,224000\\r\\nCollgCr,8,12,3,183000\\r\\nOldTown,1,8,1,93000\\r\\nGilbert,8,12,4,225000\\r\\nSWISU,7,13,1,139500\\r\\nSomerst,1,13,1,232600\\r\\nNridgHt,1,15,1,385000\\r\\nBrkSide,4,11,1,109500\\r\\nSawyerW,1,12,1,189000\\r\\nClearCr,8,10,3,185000\\r\\nSomerst,13,12,1,147400\\r\\nNAmes,1,11,4,166000\\r\\nSomerst,13,12,1,151000\\r\\nCollgCr,8,13,3,237000\\r\\nNAmes,1,12,1,167000\\r\\nNAmes,1,11,1,139950\\r\\nSawyer,1,11,1,128000\\r\\nOldTown,2,11,1,153500\\r\\nBrDale,13,11,1,100000\\r\\nCollgCr,1,11,1,144000\\r\\nEdwards,7,11,1,130500\\r\\nSWISU,9,14,1,140000\\r\\nOldTown,9,13,1,157500\\r\\nNAmes,6,13,1,174900\\r\\nNAmes,11,9,4,141000\\r\\nOldTown,7,13,1,153900\\r\\nSawyer,10,10,3,171000\\r\\nSawyerW,8,12,1,213000\\r\\nCollgCr,1,11,3,133500\\r\\nClearCr,7,14,1,240000\\r\\nVeenker,6,13,2,187000\\r\\nCollgCr,12,11,1,131500\\r\\nBlmngtn,12,13,1,215000\\r\\nCrawfor,5,12,1,164000\\r\\nNAmes,6,12,4,158000\\r\\nSawyer,1,9,1,170000\\r\\nSawyer,1,13,4,127000\\r\\nCollgCr,6,12,1,147000\\r\\nGilbert,8,11,1,174000\\r\\nNWAmes,1,12,1,152000\\r\\nNWAmes,8,13,4,250000\\r\\nCrawfor,7,15,4,189950\\r\\nSawyer,15,9,1,131500\\r\\nSawyerW,1,11,4,152000\\r\\nNAmes,1,10,1,132500\\r\\nSomerst,1,12,1,250580\\r\\nNAmes,1,11,1,148500\\r\\nTimber,1,13,1,248900\\r\\nSawyer,1,9,4,129000\\r\\nGilbert,8,11,4,169000\\r\\nSawyerW,8,12,1,236000\\r\\nNAmes,1,10,1,109500\\r\\nCollgCr,8,11,1,200500\\r\\nNAmes,1,12,4,116000\\r\\nNAmes,3,12,1,133000\\r\\nOldTown,7,11,1,66500\\r\\nSomerst,8,13,1,303477\\r\\nMitchel,1,9,1,132250\\r\\nNridgHt,8,14,1,350000\\r\\nSawyer,10,12,1,148000\\r\\nCollgCr,1,13,3,136500\\r\\nEdwards,1,10,1,157000\\r\\nTimber,7,12,3,187500\\r\\nGilbert,8,11,4,178000\\r\\nSWISU,5,9,1,118500\\r\\nNAmes,1,10,1,100000\\r\\nSomerst,12,14,3,328900\\r\\nNAmes,11,10,4,145000\\r\\nEdwards,7,12,1,135500\\r\\nNAmes,1,14,1,268000\\r\\nNAmes,1,10,1,149500\\r\\nNAmes,7,12,4,122900\\r\\nSawyer,8,11,1,172500\\r\\nSawyer,1,14,1,154500\\r\\nSawyer,1,10,1,165000\\r\\nSawyerW,11,10,1,118858\\r\\nNAmes,8,11,1,140000\\r\\nIDOTRR,2,10,1,106500\\r\\nSawyerW,11,10,1,142953\\r\\nNridgHt,1,14,1,611657\\r\\nSawyer,1,12,1,135000\\r\\nNAmes,1,10,1,110000\\r\\nNAmes,1,12,1,153000\\r\\nGilbert,8,12,1,180000\\r\\nGilbert,1,12,3,240000\\r\\nSawyer,1,11,1,125500\\r\\nNAmes,1,10,1,128000\\r\\nSomerst,1,13,4,255000\\r\\nCrawfor,7,14,1,250000\\r\\nMitchel,1,10,1,131000\\r\\nGilbert,8,11,1,174000\\r\\nNAmes,11,10,4,154300\\r\\nCollgCr,1,12,3,143500\\r\\nBrkSide,2,12,1,88000\\r\\nCrawfor,11,11,1,145000\\r\\nSomerst,13,11,1,173733\\r\\nMeadowV,13,9,1,75000\\r\\nIDOTRR,1,5,1,35311\\r\\nEdwards,1,10,1,135000\\r\\nSawyerW,8,12,4,238000\\r\\nNAmes,1,14,4,176500\\r\\nSawyerW,8,11,1,201000\\r\\nEdwards,11,12,1,145900\\r\\nGilbert,1,11,1,169990\\r\\nSawyerW,12,11,1,193000\\r\\nNWAmes,1,12,1,207500\\r\\nNWAmes,1,11,4,175000\\r\\nNridgHt,8,13,1,285000\\r\\nNWAmes,8,12,1,176000\\r\\nCollgCr,1,13,1,236500\\r\\nGilbert,8,12,4,222000\\r\\nTimber,1,13,1,201000\\r\\nNAmes,1,11,1,117500\\r\\nSomerst,1,14,4,320000\\r\\nCollgCr,1,12,2,190000\\r\\nNAmes,1,14,1,242000\\r\\nBrkSide,2,9,1,79900\\r\\nSawyerW,1,12,1,184900\\r\\nSomerst,8,12,1,253000\\r\\nCollgCr,8,12,1,239799\\r\\nClearCr,9,14,1,244400\\r\\nMitchel,11,11,1,150900\\r\\nGilbert,8,12,2,214000\\r\\nEdwards,11,7,1,150000\\r\\nMitchel,11,9,1,143000\\r\\nTimber,1,12,3,137500\\r\\nOldTown,7,11,4,124900\\r\\nNAmes,6,11,1,143000\\r\\nTimber,1,13,1,270000\\r\\nCollgCr,8,12,1,192500\\r\\nNWAmes,1,13,1,197500\\r\\nNAmes,1,13,1,129000\\r\\nSawyerW,1,10,4,119900\\r\\nCollgCr,10,13,1,133900\\r\\nMitchel,8,9,1,172000\\r\\nEdwards,11,11,3,127500\\r\\nCrawfor,11,12,1,145000\\r\\nBlueste,13,12,1,124000\\r\\nSawyer,1,10,1,132000\\r\\nCollgCr,1,12,1,185000\\r\\nSomerst,13,12,2,155000\\r\\nBrkSide,1,12,1,116500\\r\\nNWAmes,8,13,4,272000\\r\\nNPkVill,13,12,4,155000\\r\\nCollgCr,1,14,4,239000\\r\\nTimber,8,12,4,214900\\r\\nGilbert,8,11,1,178900\\r\\nCrawfor,7,12,1,160000\\r\\nNAmes,1,12,1,135000\\r\\nOldTown,7,9,1,37900\\r\\nNAmes,15,11,1,140000\\r\\nNAmes,7,8,1,135000\\r\\nNridgHt,13,12,1,173000\\r\\nSawyerW,12,11,1,99500\\r\\nSomerst,1,12,4,182000\\r\\nBrkSide,9,15,4,167500\\r\\nSomerst,13,12,2,165000\\r\\nBrkSide,2,11,1,85500\\r\\nSomerst,12,12,1,199900\\r\\nEdwards,1,9,1,110000\\r\\nSawyer,1,11,4,139000\\r\\nNAmes,10,16,4,178400\\r\\nNoRidge,8,13,4,336000\\r\\nBlmngtn,1,12,1,159895\\r\\nCollgCr,8,13,4,255900\\r\\nMitchel,11,10,1,126000\\r\\nEdwards,15,10,1,125000\\r\\nOldTown,7,14,4,117000\\r\\nNridgHt,1,14,1,395192\\r\\nNWAmes,8,12,1,195000\\r\\nSomerst,8,12,1,197000\\r\\nNoRidge,8,13,1,348000\\r\\nOldTown,9,17,4,168000\\r\\nNAmes,8,14,1,187000\\r\\nCollgCr,8,11,1,173900\\r\\nNridgHt,1,15,2,337500\\r\\nBrkSide,7,11,1,121600\\r\\nNAmes,1,11,1,136500\\r\\nNWAmes,1,12,1,185000\\r\\nIDOTRR,2,7,1,91000\\r\\nCollgCr,1,12,1,206000\\r\\nEdwards,1,6,4,82000\\r\\nOldTown,2,11,4,86000\\r\\nSomerst,1,13,1,232000\\r\\nNWAmes,11,11,4,136905\\r\\nBlmngtn,12,12,1,181000\\r\\nCollgCr,6,13,1,149900\\r\\nNAmes,1,9,1,163500\\r\\nMeadowV,13,8,1,88000\\r\\nMitchel,1,12,3,240000\\r\\nSWISU,7,10,1,102000\\r\\nSawyer,7,10,1,135000\\r\\nEdwards,11,10,1,100000\\r\\nCrawfor,9,13,1,165000\\r\\nOldTown,2,9,1,85000\\r\\nNAmes,1,11,1,119200\\r\\nNWAmes,8,14,1,227000\\r\\nCollgCr,1,12,1,203000\\r\\nStoneBr,12,13,3,187500\\r\\nGilbert,6,12,2,160000\\r\\nBlmngtn,12,12,1,213490\\r\\nEdwards,1,9,1,176000\\r\\nCollgCr,1,12,1,194000\\r\\nOldTown,7,10,1,87000\\r\\nBlmngtn,12,12,1,191000\\r\\nTimber,1,14,4,287000\\r\\nCollgCr,1,10,4,112500\\r\\nNAmes,1,10,1,167500\\r\\nTimber,1,13,1,293077\\r\\nNAmes,7,10,1,105000\\r\\nBrDale,13,13,1,118000\\r\\nSWISU,15,13,1,160000\\r\\nSWISU,5,10,4,197000\\r\\nNoRidge,8,15,4,310000\\r\\nCollgCr,1,12,1,230000\\r\\nCrawfor,2,12,1,119750\\r\\nEdwards,1,7,3,84000\\r\\nTimber,1,14,1,315500\\r\\nCollgCr,8,13,1,287000\\r\\nMeadowV,13,10,1,97000\\r\\nMeadowV,14,8,1,80000\\r\\nSawyer,1,9,4,155000\\r\\nNWAmes,8,14,1,173000\\r\\nNridgHt,12,11,1,196000\\r\\nSawyerW,8,12,1,262280\\r\\nNWAmes,1,13,1,278000\\r\\nEdwards,1,8,3,139600\\r\\nStoneBr,8,14,1,556581\\r\\nCollgCr,1,10,1,145000\\r\\nMitchel,1,9,1,115000\\r\\nEdwards,1,11,1,84900\\r\\nGilbert,1,12,1,176485\\r\\nCollgCr,1,12,4,200141\\r\\nNAmes,8,12,4,165000\\r\\nEdwards,1,11,1,144500\\r\\nCollgCr,8,13,4,255000\\r\\nNWAmes,1,11,1,180000\\r\\nNridgHt,12,12,1,185850\\r\\nNoRidge,8,13,2,248000\\r\\nNridgHt,8,14,1,335000\\r\\nCrawfor,7,13,4,220000\\r\\nStoneBr,12,13,1,213500\\r\\nIDOTRR,2,7,1,81000\\r\\nOldTown,15,10,1,90000\\r\\nOldTown,2,12,1,110500\\r\\nNAmes,1,11,3,154000\\r\\nClearCr,8,12,1,328000\\r\\nGilbert,8,13,1,178000\\r\\nNAmes,8,12,1,167900\\r\\nMeadowV,13,10,1,151400\\r\\nNAmes,4,12,1,135000\\r\\nNAmes,1,10,1,135000\\r\\nNWAmes,8,12,1,154000\\r\\nEdwards,7,8,1,91500\\r\\nEdwards,8,12,4,159500\\r\\nCollgCr,1,12,1,194000\\r\\nCrawfor,9,13,1,219500\\r\\nOldTown,7,13,1,170000\\r\\nNAmes,1,10,4,138800\\r\\nCollgCr,12,11,1,155900\\r\\nCollgCr,1,10,1,126000\\r\\nNWAmes,1,13,1,145000\\r\\nSawyer,1,10,4,133000\\r\\nCollgCr,1,12,1,192000\\r\\nNAmes,1,12,1,160000\\r\\nGilbert,8,11,4,187500\\r\\nSawyerW,10,12,1,147000\\r\\nMeadowV,13,9,1,83500\\r\\nSomerst,8,13,1,252000\\r\\nEdwards,13,12,1,137500\\r\\nSomerst,12,13,1,197000\\r\\nNAmes,11,7,4,92900\\r\\nSomerst,13,12,1,160000\\r\\nSWISU,7,11,1,136500\\r\\nNAmes,1,13,4,146000\\r\\nNAmes,1,12,1,129000\\r\\nCollgCr,1,11,1,176432\\r\\nIDOTRR,9,13,1,127000\\r\\nStoneBr,12,13,1,170000\\r\\nBrkSide,7,10,1,128000\\r\\nNWAmes,1,12,1,157000\\r\\nSWISU,2,7,1,60000\\r\\nNAmes,1,10,1,119500\\r\\nNAmes,1,12,1,135000\\r\\nNAmes,1,12,4,159500\\r\\nBrDale,13,10,1,106000\\r\\nNoRidge,8,13,4,325000\\r\\nSawyerW,1,12,4,179900\\r\\nGilbert,8,12,3,274725\\r\\nGilbert,8,11,1,181000\\r\\nNridgHt,1,13,1,280000\\r\\nGilbert,8,11,1,188000\\r\\nNWAmes,8,13,1,205000\\r\\nNAmes,1,12,4,129900\\r\\nNAmes,1,12,1,134500\\r\\nOldTown,1,12,1,117000\\r\\nNridgHt,1,13,1,318000\\r\\nGilbert,6,13,1,184100\\r\\nSawyer,1,12,2,130000\\r\\nSawyer,6,11,1,140000\\r\\nNAmes,1,10,1,133700\\r\\nIDOTRR,2,11,1,118400\\r\\nCollgCr,1,12,1,212900\\r\\nEdwards,1,7,4,112000\\r\\nEdwards,1,14,1,118000\\r\\nGilbert,6,12,1,163900\\r\\nNAmes,1,9,1,115000\\r\\nBlmngtn,12,12,1,174000\\r\\nGilbert,1,12,4,259000\\r\\nGilbert,8,12,1,215000\\r\\nOldTown,11,10,1,140000\\r\\nSWISU,7,7,1,135000\\r\\nMitchel,1,10,1,93500\\r\\nOldTown,9,10,1,117500\\r\\nSawyerW,8,13,1,239500\\r\\nGilbert,8,11,1,169000\\r\\nBrkSide,2,11,4,102000\\r\\nNAmes,7,11,1,119000\\r\\nSawyer,7,13,1,94000\\r\\nMitchel,1,11,1,196000\\r\\nBrkSide,2,10,1,144000\\r\\nNAmes,1,12,4,139000\\r\\nNWAmes,8,12,3,197500\\r\\nCollgCr,8,13,1,424870\\r\\nSawyer,1,8,1,80000\\r\\nEdwards,15,8,1,80000\\r\\nBrkSide,7,11,1,149000\\r\\nSawyerW,1,11,1,180000\\r\\nCrawfor,9,14,1,174500\\r\\nOldTown,7,14,1,116900\\r\\nOldTown,9,16,1,143000\\r\\nNAmes,1,11,1,124000\\r\\nEdwards,1,9,1,149900\\r\\nCrawfor,1,13,1,230000\\r\\nIDOTRR,2,14,4,120500\\r\\nNAmes,8,13,1,201800\\r\\nVeenker,1,13,4,218000\\r\\nNAmes,6,13,1,179900\\r\\nNridgHt,12,12,1,230000\\r\\nSomerst,1,13,2,235128\\r\\nNWAmes,8,12,1,185000\\r\\nNPkVill,13,11,1,146000\\r\\nCrawfor,1,13,3,224000\\r\\nSawyer,1,10,1,129000\\r\\nSawyer,11,8,1,108959\\r\\nVeenker,6,12,2,194000\\r\\nNridgHt,1,12,1,233170\\r\\nCollgCr,1,13,4,245350\\r\\nGilbert,8,11,1,173000\\r\\nEdwards,9,13,4,235000\\r\\nNoRidge,8,15,3,625000\\r\\nMitchel,6,12,1,171000\\r\\nNAmes,1,12,1,163000\\r\\nSomerst,13,12,1,171900\\r\\nClearCr,7,11,1,200500\\r\\nCrawfor,9,14,1,239000\\r\\nNoRidge,7,13,1,285000\\r\\nMitchel,1,10,3,119500\\r\\nOldTown,7,14,1,115000\\r\\nCrawfor,7,11,2,154900\\r\\nEdwards,1,10,4,93000\\r\\nTimber,8,12,4,250000\\r\\nCrawfor,12,13,1,392500\\r\\nNoRidge,8,15,4,745000\\r\\nOldTown,2,11,1,120000\\r\\nTimber,1,9,1,186700\\r\\nEdwards,7,12,1,104900\\r\\nOldTown,15,8,4,95000\\r\\nNoRidge,1,13,4,262000\\r\\nCollgCr,8,12,1,195000\\r\\nGilbert,8,12,1,189000\\r\\nMitchel,15,8,1,168000\\r\\nSomerst,13,13,1,174000\\r\\nOldTown,7,13,1,125000\\r\\nMitchel,12,11,2,165000\\r\\nSawyer,8,13,4,158000\\r\\nGilbert,8,11,1,176000\\r\\nGilbert,8,12,1,219210\\r\\nOldTown,5,13,4,144000\\r\\nCollgCr,1,12,1,178000\\r\\nSawyer,1,9,1,148000\\r\\nNAmes,1,9,1,116050\\r\\nCollgCr,8,12,4,197900\\r\\nBrkSide,7,13,4,117000\\r\\nCollgCr,1,12,1,213000\\r\\nNWAmes,1,11,1,153500\\r\\nSawyerW,1,14,3,271900\\r\\nSawyerW,1,8,1,107000\\r\\nCollgCr,1,11,1,200000\\r\\nNAmes,1,12,1,140000\\r\\nSomerst,1,13,3,290000\\r\\nSawyerW,8,11,1,189000\\r\\nGilbert,7,15,1,164000\\r\\nEdwards,2,10,1,113000\\r\\nSawyer,6,13,3,145000\\r\\nNAmes,10,10,1,134500\\r\\nSawyer,1,10,1,125000\\r\\nSawyer,11,11,1,112000\\r\\nSomerst,1,13,1,229456\\r\\nBrkSide,7,9,1,80500\\r\\nBrDale,13,11,1,91500\\r\\nNAmes,1,10,1,115000\\r\\nSawyer,1,10,1,134000\\r\\nNAmes,7,12,1,143000\\r\\nNAmes,1,8,1,137900\\r\\nGilbert,8,12,1,184000\\r\\nNAmes,6,14,1,145000\\r\\nSomerst,8,11,3,214000\\r\\nNAmes,1,13,4,147000\\r\\nNridgHt,12,14,4,367294\\r\\nNAmes,6,10,1,127000\\r\\nSawyer,11,10,1,190000\\r\\nNAmes,11,11,1,132500\\r\\nNAmes,11,9,2,101800\\r\\nNAmes,1,10,1,142000\\r\\nSWISU,9,11,1,130000\\r\\nCrawfor,9,10,1,138887\\r\\nNridgHt,13,12,1,175500\\r\\nCollgCr,8,12,2,195000\\r\\nSawyerW,1,11,1,142500\\r\\nTimber,1,13,1,265900\\r\\nCollgCr,8,12,1,224900\\r\\nSomerst,1,13,1,248328\\r\\nNWAmes,10,13,1,170000\\r\\nNridgHt,1,15,1,465000\\r\\nCrawfor,9,15,4,230000\\r\\nNWAmes,6,13,1,178000\\r\\nSomerst,8,12,1,186500\\r\\nMitchel,6,11,1,169900\\r\\nOldTown,5,11,1,129500\\r\\nNAmes,1,12,1,119000\\r\\nNAmes,1,12,4,244000\\r\\nNridgHt,12,12,4,171750\\r\\nMitchel,1,11,1,130000\\r\\nVeenker,8,14,1,294000\\r\\nEdwards,8,12,1,165400\\r\\nBrkSide,7,12,1,127500\\r\\nNoRidge,1,13,4,301500\\r\\nEdwards,2,13,4,99900\\r\\nGilbert,6,12,1,190000\\r\\nNAmes,1,14,2,151000\\r\\nGilbert,8,11,3,181000\\r\\nNAmes,1,11,1,128900\\r\\nClearCr,7,9,1,161500\\r\\nBrkSide,9,12,1,180500\\r\\nNAmes,12,11,1,181000\\r\\nSomerst,13,12,5,183900\\r\\nOldTown,15,11,1,122000\\r\\nTimber,1,14,1,378500\\r\\nCrawfor,7,17,1,381000\\r\\nNAmes,7,10,1,144000\\r\\nClearCr,3,13,1,260000\\r\\nNWAmes,1,13,1,185750\\r\\nSawyer,1,11,3,137000\\r\\nEdwards,6,13,4,177000\\r\\nCrawfor,7,11,4,139000\\r\\nNAmes,11,10,4,137000\\r\\nNWAmes,8,12,3,162000\\r\\nNWAmes,6,11,3,197900\\r\\nCollgCr,8,13,1,237000\\r\\nIDOTRR,7,8,1,68400\\r\\nCollgCr,1,12,1,227000\\r\\nTimber,1,12,3,180000\\r\\nCollgCr,1,12,1,150500\\r\\nMitchel,11,11,4,139000\\r\\nSWISU,7,13,1,169000\\r\\nBrkSide,7,12,1,132500\\r\\nNWAmes,1,11,1,143000\\r\\nClearCr,1,10,1,190000\\r\\nStoneBr,12,13,1,278000\\r\\nNridgHt,8,13,1,281000\\r\\nNAmes,6,12,4,180500\\r\\nBrDale,13,12,1,119500\\r\\nOldTown,9,9,4,107500\\r\\nNWAmes,8,12,1,162900\\r\\nEdwards,1,12,1,115000\\r\\nNAmes,1,10,1,138500\\r\\nNAmes,1,11,1,155000\\r\\nEdwards,14,11,1,140000\\r\\nEdwards,8,15,4,160000\\r\\nNAmes,1,12,1,154000\\r\\nGilbert,8,12,3,225000\\r\\nCrawfor,9,13,1,177500\\r\\nNoRidge,8,13,1,290000\\r\\nSomerst,1,12,1,232000\\r\\nEdwards,13,12,1,130000\\r\\nNridgHt,1,14,4,325000\\r\\nNridgHt,12,12,1,202500\\r\\nCollgCr,1,10,1,138000\\r\\nOldTown,1,12,1,147000\\r\\nSawyerW,1,11,1,179200\\r\\nCrawfor,1,15,1,335000\\r\\nCollgCr,1,12,1,203000\\r\\nNoRidge,8,13,1,302000\\r\\nNoRidge,8,14,4,333168\\r\\nEdwards,1,10,1,119000\\r\\nNAmes,8,11,1,206900\\r\\nCollgCr,1,13,1,295493\\r\\nSomerst,12,12,4,208900\\r\\nCollgCr,1,13,3,275000\\r\\nEdwards,1,9,1,111000\\r\\nNAmes,1,9,1,156500\\r\\nBrkSide,1,9,4,72500\\r\\nNoRidge,8,12,1,190000\\r\\nBrkSide,2,11,1,82500\\r\\nSomerst,1,13,1,147000\\r\\nIDOTRR,2,8,1,55000\\r\\nEdwards,2,9,1,79000\\r\\nMitchel,1,14,1,130500\\r\\nOldTown,7,13,4,256000\\r\\nGilbert,8,12,1,176500\\r\\nSomerst,1,13,1,227000\\r\\nCollgCr,6,10,1,132500\\r\\nEdwards,1,10,1,100000\\r\\nIDOTRR,7,11,4,125500\\r\\nBrDale,13,11,1,125000\\r\\nNWAmes,1,11,1,167900\\r\\nNWAmes,11,10,1,135000\\r\\nOldTown,2,8,4,52500\\r\\nCollgCr,8,12,4,200000\\r\\nCollgCr,1,12,4,128500\\r\\nNAmes,1,9,1,123000\\r\\nSawyerW,1,11,1,155000\\r\\nCollgCr,8,13,1,228500\\r\\nCrawfor,7,12,1,177000\\r\\nCollgCr,8,12,4,155835\\r\\nOldTown,2,8,1,108500\\r\\nNWAmes,1,14,3,262500\\r\\nTimber,1,13,4,283463\\r\\nSawyerW,1,12,1,215000\\r\\nOldTown,9,13,1,122000\\r\\nNAmes,11,10,1,200000\\r\\nNAmes,8,12,1,171000\\r\\nBrkSide,7,15,1,134900\\r\\nNoRidge,7,13,3,410000\\r\\nCollgCr,8,12,1,235000\\r\\nNWAmes,6,13,1,170000\\r\\nNAmes,1,10,4,110000\\r\\nNAmes,1,11,3,149900\\r\\nSomerst,13,11,1,177500\\r\\nNridgHt,1,14,5,315000\\r\\nSWISU,9,11,1,189000\\r\\nStoneBr,1,12,1,260000\\r\\nNAmes,7,8,2,104900\\r\\nGilbert,8,11,1,156932\\r\\nSomerst,13,12,1,144152\\r\\nSomerst,8,12,1,216000\\r\\nCollgCr,8,12,1,193000\\r\\nMeadowV,13,11,1,127000\\r\\nCollgCr,12,11,1,144000\\r\\nCollgCr,1,13,2,232000\\r\\nOldTown,7,10,4,105000\\r\\nNAmes,6,12,1,165500\\r\\nCollgCr,8,13,4,274300\\r\\nNoRidge,1,15,1,466500\\r\\nSomerst,8,12,1,250000\\r\\nTimber,1,13,1,239000\\r\\nSWISU,2,11,1,91000\\r\\nEdwards,7,10,1,117000\\r\\nBrDale,13,11,1,83000\\r\\nTimber,6,10,1,167500\\r\\nEdwards,2,6,1,58500\\r\\nNAmes,1,13,4,237500\\r\\nOldTown,9,14,4,157000\\r\\nSawyer,2,12,1,112000\\r\\nEdwards,7,11,1,105000\\r\\nIDOTRR,7,12,1,125500\\r\\nNWAmes,8,12,1,250000\\r\\nOldTown,7,13,1,136000\\r\\nGilbert,1,14,3,377500\\r\\nBrkSide,7,12,1,131000\\r\\nCollgCr,1,12,1,235000\\r\\nNAmes,11,10,1,124000\\r\\nNAmes,10,10,1,123000\\r\\nOldTown,15,13,1,163000\\r\\nBlmngtn,12,12,1,246578\\r\\nTimber,8,13,1,281213\\r\\nTimber,1,10,1,160000\\r\\nBrkSide,9,13,1,137500\\r\\nNAmes,7,9,1,138000\\r\\nSWISU,7,12,1,137450\\r\\nBrkSide,7,13,4,120000\\r\\nGilbert,8,11,1,193000\\r\\nCollgCr,1,12,1,193879\\r\\nSomerst,1,13,3,282922\\r\\nOldTown,7,7,4,105000\\r\\nCrawfor,12,13,1,275000\\r\\nCollgCr,10,12,4,133000\\r\\nMitchel,1,10,4,112000\\r\\nOldTown,9,11,1,125500\\r\\nNWAmes,8,13,3,215000\\r\\nCollgCr,8,12,1,230000\\r\\nNAmes,7,14,1,140000\\r\\nNAmes,11,9,1,90000\\r\\nSawyerW,1,13,4,257000\\r\\nBrkSide,7,13,1,207000\\r\\nBlmngtn,12,12,1,175900\\r\\nOldTown,15,10,1,122500\\r\\nNoRidge,8,13,1,340000\\r\\nNAmes,1,10,1,124000\\r\\nCrawfor,1,11,1,223000\\r\\nNWAmes,8,12,4,179900\\r\\nNPkVill,12,11,1,127500\\r\\nCollgCr,12,11,1,136500\\r\\nEdwards,6,13,3,274970\\r\\nNAmes,1,10,1,144000\\r\\nNAmes,1,12,1,142000\\r\\nNoRidge,8,12,1,271000\\r\\nNAmes,7,11,1,140000\\r\\nOldTown,2,12,4,119000\\r\\nNWAmes,1,13,4,182900\\r\\nGilbert,8,10,1,192140\\r\\nNPkVill,12,12,1,143750\\r\\nOldTown,2,10,1,64500\\r\\nGilbert,8,11,1,186500\\r\\nMitchel,1,10,1,160000\\r\\nNAmes,1,15,1,174000\\r\\nNAmes,1,10,2,120500\\r\\nNridgHt,1,13,2,394617\\r\\nOldTown,1,13,1,149700\\r\\nNWAmes,8,13,1,197000\\r\\nCrawfor,9,13,1,191000\\r\\nCollgCr,12,11,1,149300\\r\\nSomerst,8,15,1,310000\\r\\nBrkSide,2,12,1,121000\\r\\nCollgCr,1,12,2,179600\\r\\nSawyer,10,11,1,129000\\r\\nMitchel,1,12,3,157900\\r\\nCollgCr,8,13,1,240000\\r\\nEdwards,7,11,1,112000\\r\\nMeadowV,14,12,1,92000\\r\\nNAmes,11,10,2,136000\\r\\nSomerst,1,13,1,287090\\r\\nEdwards,14,10,1,145000\\r\\nMitchel,1,10,1,84500\\r\\nSomerst,1,12,1,185000\\r\\nGilbert,8,11,1,175000\\r\\nNWAmes,1,12,1,210000\\r\\nCrawfor,9,16,1,266500\\r\\nNAmes,1,11,1,142125\\r\\nEdwards,1,11,1,147500\\r\\n'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "MckK-fA1OLmZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1105
        },
        "outputId": "cca64ea1-3988-4101-bff7-ef6edeb4d48f"
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('project_data.csv')\n",
        "temp_copy=df\n",
        "print(df)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Geography  vendor  Risk  delivery_mix     Bid\n",
            "0      CollgCr       8    12             1  208500\n",
            "1      Veenker       1    12             2  181500\n",
            "2      CollgCr       8    12             1  223500\n",
            "3      Crawfor       9    12             4  140000\n",
            "4      NoRidge       8    12             2  250000\n",
            "5      Mitchel       7    12             1  143000\n",
            "6      Somerst       1    12             1  307000\n",
            "7       NWAmes       8    12             4  200000\n",
            "8      OldTown       7    12             1  129900\n",
            "9      BrkSide      15    12             4  118000\n",
            "10      Sawyer       1    12             1  129500\n",
            "11     NridgHt       8    14             1  345000\n",
            "12      Sawyer       1    11             1  144000\n",
            "13     CollgCr       1    12             1  279500\n",
            "14       NAmes       1    11             4  157000\n",
            "15     BrkSide       4    15             4  132000\n",
            "16       NAmes       1    13             3  149000\n",
            "17      Sawyer      11     9             1   90000\n",
            "18     SawyerW       1    10             1  159000\n",
            "19       NAmes       1    11             1  139000\n",
            "20     NridgHt       8    13             4  325300\n",
            "21      IDOTRR       4    14             1  139400\n",
            "22     CollgCr       1    13             1  230000\n",
            "23     MeadowV      12    12             1  129900\n",
            "24      Sawyer       1    13             1  154000\n",
            "25     NridgHt       1    13             4  256300\n",
            "26       NAmes       1    12             4  134800\n",
            "27     NridgHt       1    13             1  306000\n",
            "28       NAmes       1    11             3  207500\n",
            "29     BrkSide       2    10             1   68500\n",
            "...        ...     ...   ...           ...     ...\n",
            "1430   Gilbert       8    10             1  192140\n",
            "1431   NPkVill      12    12             1  143750\n",
            "1432   OldTown       2    10             1   64500\n",
            "1433   Gilbert       8    11             1  186500\n",
            "1434   Mitchel       1    10             1  160000\n",
            "1435     NAmes       1    15             1  174000\n",
            "1436     NAmes       1    10             2  120500\n",
            "1437   NridgHt       1    13             2  394617\n",
            "1438   OldTown       1    13             1  149700\n",
            "1439    NWAmes       8    13             1  197000\n",
            "1440   Crawfor       9    13             1  191000\n",
            "1441   CollgCr      12    11             1  149300\n",
            "1442   Somerst       8    15             1  310000\n",
            "1443   BrkSide       2    12             1  121000\n",
            "1444   CollgCr       1    12             2  179600\n",
            "1445    Sawyer      10    11             1  129000\n",
            "1446   Mitchel       1    12             3  157900\n",
            "1447   CollgCr       8    13             1  240000\n",
            "1448   Edwards       7    11             1  112000\n",
            "1449   MeadowV      14    12             1   92000\n",
            "1450     NAmes      11    10             2  136000\n",
            "1451   Somerst       1    13             1  287090\n",
            "1452   Edwards      14    10             1  145000\n",
            "1453   Mitchel       1    10             1   84500\n",
            "1454   Somerst       1    12             1  185000\n",
            "1455   Gilbert       8    11             1  175000\n",
            "1456    NWAmes       1    12             1  210000\n",
            "1457   Crawfor       9    16             1  266500\n",
            "1458     NAmes       1    11             1  142125\n",
            "1459   Edwards       1    11             1  147500\n",
            "\n",
            "[1460 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OuFUe_NsOLhH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1244
        },
        "outputId": "445995b8-0a69-4378-bc58-e1a27345dfe6"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df= temp_copy\n",
        "bid= df['Bid']\n",
        "df = df.drop('Bid', axis=1)\n",
        "\n",
        "\n",
        "col_names = df.select_dtypes(exclude=['object']).columns\n",
        "print(col_names)\n",
        "features = df[col_names]\n",
        "# print(features.head())\n",
        "scaler = MinMaxScaler().fit(features.values)\n",
        "features = scaler.transform(features.values)\n",
        "df[col_names] = features\n",
        "\n",
        "df[df.select_dtypes(include=['object']).columns] = df.select_dtypes(include=['object']).apply(lambda x: x.astype('category')).apply(lambda x: x.cat.codes)\n",
        "\n",
        "# print(categorical_features.head())\n",
        "print(df.head())\n",
        "print(bid)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['vendor', 'Risk', 'delivery_mix'], dtype='object')\n",
            "   Geography    vendor      Risk  delivery_mix\n",
            "0          5  0.500000  0.588235          0.00\n",
            "1         24  0.000000  0.588235          0.25\n",
            "2          5  0.500000  0.588235          0.00\n",
            "3          6  0.571429  0.588235          0.75\n",
            "4         15  0.500000  0.588235          0.25\n",
            "0       208500\n",
            "1       181500\n",
            "2       223500\n",
            "3       140000\n",
            "4       250000\n",
            "5       143000\n",
            "6       307000\n",
            "7       200000\n",
            "8       129900\n",
            "9       118000\n",
            "10      129500\n",
            "11      345000\n",
            "12      144000\n",
            "13      279500\n",
            "14      157000\n",
            "15      132000\n",
            "16      149000\n",
            "17       90000\n",
            "18      159000\n",
            "19      139000\n",
            "20      325300\n",
            "21      139400\n",
            "22      230000\n",
            "23      129900\n",
            "24      154000\n",
            "25      256300\n",
            "26      134800\n",
            "27      306000\n",
            "28      207500\n",
            "29       68500\n",
            "         ...  \n",
            "1430    192140\n",
            "1431    143750\n",
            "1432     64500\n",
            "1433    186500\n",
            "1434    160000\n",
            "1435    174000\n",
            "1436    120500\n",
            "1437    394617\n",
            "1438    149700\n",
            "1439    197000\n",
            "1440    191000\n",
            "1441    149300\n",
            "1442    310000\n",
            "1443    121000\n",
            "1444    179600\n",
            "1445    129000\n",
            "1446    157900\n",
            "1447    240000\n",
            "1448    112000\n",
            "1449     92000\n",
            "1450    136000\n",
            "1451    287090\n",
            "1452    145000\n",
            "1453     84500\n",
            "1454    185000\n",
            "1455    175000\n",
            "1456    210000\n",
            "1457    266500\n",
            "1458    142125\n",
            "1459    147500\n",
            "Name: Bid, Length: 1460, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
            "  warnings.warn(msg, DataConversionWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "pLce5XktenN-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd9d7d5d-0ab0-419d-e5bd-acbf51cf0b35"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "X = df.as_matrix()\n",
        "test_n = df.shape[0]\n",
        "pca = PCA()\n",
        "pca.fit(X[:test_n,:], bid)\n",
        "X = pca.transform(X)\n",
        "X.shape"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "metadata": {
        "id": "7x2vlJUydF0U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "488dacbe-e140-4f5b-9666-9eef99ae6cc2"
      },
      "cell_type": "code",
      "source": [
        "# X_train = X[:test_n,:]\n",
        "X_train, X_test, y_train, y_test = ms.train_test_split(X, bid, test_size=0.2, random_state=0)\n",
        "(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1168, 4), (292, 4), (1168,), (292,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "metadata": {
        "id": "gHVacX59OLdj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1428
        },
        "outputId": "0da0b6a5-467b-40a8-c043-dcf51eadfb22"
      },
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "# lr = linear_model.LinearRegression()\n",
        "regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=100)\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "prediction= regr.predict(X_test)\n",
        "print(y_test[0:10], prediction)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "529     200624\n",
            "491     133000\n",
            "459     110000\n",
            "279     192000\n",
            "655      88000\n",
            "1013     85000\n",
            "1403    282922\n",
            "601     141000\n",
            "1182    745000\n",
            "687     148800\n",
            "Name: Bid, dtype: int64 [125091.26729613 205502.23643814 125091.26729613 183123.36654785\n",
            " 152511.45940903 125091.26729613 271009.63709754 172939.44332453\n",
            " 278760.48714549 174168.15339972 156493.61418859 152039.52378955\n",
            " 172939.44332453 125091.26729613 152511.45940903 152511.45940903\n",
            " 173460.05167971 125091.26729613 152039.52378955 278760.48714549\n",
            " 125091.26729613 152511.45940903 125091.26729613 152511.45940903\n",
            " 172939.44332453 278760.48714549 152039.52378955 125091.26729613\n",
            " 207198.33299346 125445.77729975 125091.26729613 173460.05167971\n",
            " 172939.44332453 173460.05167971 271009.63709754 172939.44332453\n",
            " 271710.32678797 130892.23056655 174168.15339972 271009.63709754\n",
            " 205502.23643814 125445.77729975 196001.76889997 271009.63709754\n",
            " 271009.63709754 125091.26729613 173460.05167971 152511.45940903\n",
            " 152039.52378955 154343.82309087 271710.32678797 172939.44332453\n",
            " 152511.45940903 125091.26729613 173460.05167971 125091.26729613\n",
            " 173460.05167971 205502.23643814 152039.52378955 125091.26729613\n",
            " 205502.23643814 125445.77729975 152511.45940903 172939.44332453\n",
            " 152039.52378955 125091.26729613 172939.44332453 171702.24887353\n",
            " 152039.52378955 172939.44332453 172939.44332453 172939.44332453\n",
            " 125091.26729613 271710.32678797 125091.26729613 271009.63709754\n",
            " 125091.26729613 172364.34755865 172939.44332453 205502.23643814\n",
            " 125091.26729613 205502.23643814 172939.44332453 145918.79889196\n",
            " 154343.82309087 196001.76889997 125091.26729613 173460.05167971\n",
            " 152511.45940903 205502.23643814 152511.45940903 152039.52378955\n",
            " 152039.52378955 172939.44332453 189241.96909077 152039.52378955\n",
            " 196001.76889997 205502.23643814 152511.45940903 183123.36654785\n",
            " 196001.76889997 172939.44332453 154343.82309087 172939.44332453\n",
            " 271710.32678797 271009.63709754 172939.44332453 134303.69600871\n",
            " 152511.45940903 205502.23643814 154822.30406623 125091.26729613\n",
            " 152039.52378955 173460.05167971 154182.76953139 172939.44332453\n",
            " 278760.48714549 125091.26729613 172939.44332453 271009.63709754\n",
            " 173460.05167971 156015.13321323 125091.26729613 172939.44332453\n",
            " 196001.76889997 174168.15339972 271710.32678797 174168.15339972\n",
            " 174168.15339972 152511.45940903 172939.44332453 196001.76889997\n",
            " 125091.26729613 254698.20282837 125091.26729613 172939.44332453\n",
            " 271009.63709754 173460.05167971 152039.52378955 152039.52378955\n",
            " 207198.33299346 125445.77729975 152511.45940903 196001.76889997\n",
            " 125091.26729613 152511.45940903 271009.63709754 172939.44332453\n",
            " 271710.32678797 173460.05167971 152511.45940903 271009.63709754\n",
            " 207198.33299346 172939.44332453 278760.48714549 172939.44332453\n",
            " 152039.52378955 213253.08648609 130403.14300511 172939.44332453\n",
            " 125445.77729975 125445.77729975 174168.15339972 125091.26729613\n",
            " 271009.63709754 172939.44332453 152039.52378955 173460.05167971\n",
            " 173460.05167971 152511.45940903 152039.52378955 173460.05167971\n",
            " 152039.52378955 174168.15339972 130403.14300511 172939.44332453\n",
            " 172939.44332453 205502.23643814 173460.05167971 152039.52378955\n",
            " 278760.48714549 205502.23643814 271710.32678797 271009.63709754\n",
            " 125091.26729613 172939.44332453 182627.8437164  196001.76889997\n",
            " 189241.96909077 152511.45940903 145918.79889196 205502.23643814\n",
            " 173460.05167971 125445.77729975 205502.23643814 172939.44332453\n",
            " 125091.26729613 206202.92612856 172939.44332453 135337.46940901\n",
            " 152511.45940903 264699.2007794  152511.45940903 145918.79889196\n",
            " 269975.22401068 125091.26729613 172939.44332453 207198.33299346\n",
            " 205502.23643814 125091.26729613 125091.26729613 264699.2007794\n",
            " 125091.26729613 172364.34755865 271710.32678797 152039.52378955\n",
            " 172939.44332453 173460.05167971 271009.63709754 152039.52378955\n",
            " 172939.44332453 125091.26729613 271710.32678797 172939.44332453\n",
            " 189241.96909077 173460.05167971 205502.23643814 172939.44332453\n",
            " 173460.05167971 172939.44332453 125091.26729613 278760.48714549\n",
            " 125091.26729613 172364.34755865 271009.63709754 183123.36654785\n",
            " 125091.26729613 173460.05167971 125091.26729613 152511.45940903\n",
            " 173460.05167971 152039.52378955 182627.8437164  182627.8437164\n",
            " 174168.15339972 264699.2007794  240901.23308268 182627.8437164\n",
            " 173546.06586699 196001.76889997 172939.44332453 271009.63709754\n",
            " 205502.23643814 272705.73365287 271009.63709754 196001.76889997\n",
            " 271710.32678797 189241.96909077 243934.12820486 213253.08648609\n",
            " 264699.2007794  125091.26729613 125091.26729613 152039.52378955\n",
            " 278760.48714549 271710.32678797 213253.08648609 151535.58745193\n",
            " 207198.33299346 205502.23643814 271009.63709754 213253.08648609\n",
            " 145488.94290853 152039.52378955 173460.05167971 173460.05167971\n",
            " 213253.08648609 205502.23643814 172939.44332453 125445.77729975\n",
            " 172939.44332453 271710.32678797 205502.23643814 152039.52378955\n",
            " 196001.76889997 125091.26729613 205502.23643814 206338.50930699\n",
            " 278760.48714549 271009.63709754 172364.34755865 172939.44332453]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CyDIAkwnOLNu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "546a86fa-52f8-4fe6-cee9-2e36bfbc4d08"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot( y_test, 'b.')\n",
        "ax.plot(prediction,'r.')\n",
        "# ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--')\n",
        "ax.set_xlabel('Measured')\n",
        "ax.set_ylabel('Predicted')\n",
        "ax.set_title('RandomForest')\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pJSUl5OTkAJCZmYnRaMRqtVJRUYHBYABg9OjRHD58mJKSErKzs9FoNMTGxpKYmIjJZAp1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\nJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9829ba45c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FNZvnmJ2jyWl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sts3wnvdjySh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TOPyeb_BjyOs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X35qN0HijyKp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-kDVt48PjyHf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CtBdGqbXjyE9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xx-H3CQEjx9S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0V8ujsdd-RW_",
        "colab_type": "code",
        "outputId": "9178eebe-eb6a-4606-e3cb-b4e798ec3bf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "# from csv import DictReader\n",
        "# with open(\"SelectedColumns.csv\") as f:\n",
        "#     a1 = [row for row in DictReader(f)]\n",
        "\n",
        "# print(a1)\n",
        "# import csv\n",
        "# with open(\"SelectedColumns.csv\", \"rb\") as f:\n",
        "  \n",
        "#     reader = csv.reader(f)\n",
        "#     i = next(reader)\n",
        "\n",
        "#     print(i)\n",
        " \n",
        "import pandas\n",
        "\n",
        "total_data=[]\n",
        "bids=None\n",
        "data = pandas.read_csv(\"SelectedColumns.csv\", header=0)\n",
        "for col in data.columns:\n",
        "  if col!='SalePrice':\n",
        "    total_data.append(eval('list(data.'+col+')'))\n",
        "  else:\n",
        "    total_bids= eval('list(data.'+col+')')\n",
        "# print(data.columns.tolist())\n",
        "# col_a = list(data.LotArea)\n",
        "# col_b = list(data.b)\n",
        "# col_c = list(data.c)\n",
        "\n",
        "print(total_data)\n",
        "print(total_bids)\n",
        "\n",
        "\n",
        "datalength= len(total_bids)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[8450, 9600, 11250, 9550, 14260, 14115, 10084, 10382, 6120, 7420, 11200, 11924, 12968, 10652, 10920, 6120, 11241, 10791, 13695, 7560, 14215, 7449, 9742, 4224, 8246, 14230, 7200, 11478, 16321, 6324, 8500, 8544, 11049, 10552, 7313, 13418, 10859, 8532, 7922, 6040, 8658, 16905, 9180, 9200, 7945, 7658, 12822, 11096, 4456, 7742, 13869, 6240, 8472, 50271, 7134, 10175, 2645, 11645, 13682, 7200, 13072, 7200, 6442, 10300, 9375, 9591, 19900, 10665, 4608, 15593, 13651, 7599, 10141, 10200, 5790, 1596, 8475, 8635, 10778, 10440, 13000, 4500, 10206, 8892, 8530, 16059, 11911, 3951, 8470, 8070, 7200, 8500, 13360, 7200, 9337, 9765, 10264, 10921, 10625, 9320, 10603, 9206, 7018, 10402, 7758, 9375, 10800, 6000, 8500, 11751, 9525, 7750, 9965, 21000, 7259, 3230, 11616, 8536, 12376, 8461, 21453, 6060, 9464, 7892, 17043, 6780, 4928, 4388, 7590, 8973, 14200, 12224, 7388, 6853, 10335, 10400, 10355, 11070, 9066, 15426, 10500, 11645, 8520, 10335, 9100, 2522, 6120, 9505, 7500, 6240, 10356, 13891, 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112000, 105000, 125500, 250000, 136000, 377500, 131000, 235000, 124000, 123000, 163000, 246578, 281213, 160000, 137500, 138000, 137450, 120000, 193000, 193879, 282922, 105000, 275000, 133000, 112000, 125500, 215000, 230000, 140000, 90000, 257000, 207000, 175900, 122500, 340000, 124000, 223000, 179900, 127500, 136500, 274970, 144000, 142000, 271000, 140000, 119000, 182900, 192140, 143750, 64500, 186500, 160000, 174000, 120500, 394617, 149700, 197000, 191000, 149300, 310000, 121000, 179600, 129000, 157900, 240000, 112000, 92000, 136000, 287090, 145000, 84500, 185000, 175000, 210000, 266500, 142125, 147500]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xPUuv-ulBJqj",
        "colab_type": "code",
        "outputId": "acbcc7d5-bcd1-4457-a35a-77ccdbd0dab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "cell_type": "code",
      "source": [
        "# characteristics = [[2,4,6,3,2,4,6,8,2,4],[222,444,233,112,443,532,223,297,211,328],[1000.1,234234.3,23434.5,46555.88, 78345.5,4566.6,22333.5,7777.3,657.3,67878.3],[\"canada\",\"australia\",\"canada\",\"india\",\"london\",\"canada\",\"india\",\"australia\",\"london\",\"india\"]]\n",
        "# bids = [112, 3333, 2312, 998, 768, 470, 123, 6546, 866, 911]\n",
        "characteristics=[]\n",
        "test_data=[]\n",
        "for ch in total_data:\n",
        "  characteristics.append(ch[0:int(datalength*0.9)])\n",
        "  test_data.append(ch[int(datalength*0.9):datalength])\n",
        "\n",
        "bids=total_bids[0:int(datalength*0.9)]\n",
        "\n",
        "\n",
        "print(characteristics)\n",
        "print(test_data)\n",
        "print(bids)\n",
        "\n",
        "print(len(bids))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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99900, 190000, 151000, 181000, 128900, 161500, 180500, 181000, 183900, 122000, 378500, 381000, 144000, 260000, 185750, 137000, 177000, 139000, 137000, 162000, 197900, 237000, 68400, 227000, 180000, 150500, 139000, 169000, 132500, 143000, 190000, 278000, 281000, 180500, 119500, 107500, 162900, 115000, 138500, 155000, 140000, 160000, 154000, 225000, 177500, 290000, 232000, 130000, 325000, 202500, 138000, 147000, 179200, 335000, 203000, 302000, 333168]\n",
            "1314\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MaMe0n5XniNq",
        "colab_type": "code",
        "outputId": "019ecd71-5b2e-476b-a69b-f9409d626971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import preprocessing\n",
        "from sklearn import linear_model\n",
        "import numpy as np\n",
        "import numbers\n",
        "\n",
        "def normalize(x_array):\n",
        "    x_array = np.asarray(x_array)\n",
        "    return (x_array - min(x_array))/(max(x_array)-min(x_array))\n",
        "\n",
        "def create_records(x_array):\n",
        "    return np.transpose(x_array)\n",
        "\n",
        "def convert_to_numeric(x_array, test = None):\n",
        "    le = preprocessing.LabelEncoder()\n",
        "    le.fit(np.unique(x_array))\n",
        "    if test is None:\n",
        "        return le.transform(x_array)\n",
        "    else:\n",
        "        return le.transform(test)\n",
        "\n",
        "def correctify_data(characteristics):\n",
        "    min_length = len(characteristics[0])\n",
        "    print(min_length)\n",
        "    new_characteristics=[]\n",
        "    for characteristic in characteristics:\n",
        "        if len(characteristic) < min_length:\n",
        "            min_length = len(characteristic)\n",
        "\n",
        "\n",
        "    for characteristic in characteristics:\n",
        "        if type(characteristic[0]) == type(\"string\"):\n",
        "            numeric_labels = convert_to_numeric(characteristic[:min_length])\n",
        "            new_characteristics.append(normalize(numeric_labels))  # confused about this\n",
        "        else:\n",
        "            new_characteristics.append(normalize(characteristic[:min_length]))\n",
        "    return new_characteristics\n",
        "\n",
        "def correctify_test_data(characteristics, test_data):\n",
        "    new_test_data=[]\n",
        "    for i in range(len(test_data)):\n",
        "\n",
        "        if not isinstance(test_data[i], numbers.Number) :\n",
        "            numeric_labels = convert_to_numeric(characteristics[i])\n",
        "            numeric_test = convert_to_numeric(characteristics[i], test=[test_data[i]])\n",
        "            new_test_data.append((numeric_test[0] - min(numeric_labels)) / (max(numeric_labels) - min(numeric_labels)))\n",
        "        else:\n",
        "            new_test_data.append((test_data[i] - min(characteristics[i])) / (max(characteristics[i]) - min(characteristics[i])))\n",
        "    return new_test_data\n",
        "\n",
        "# characteristics = [[2,4,6,3,2,4,6,8,2,4],[222,444,233,112,443,532,223,297,211,328],[1000.1,234234.3,23434.5,46555.88, 78345.5,4566.6,22333.5,7777.3,657.3,67878.3],[\"canada\",\"australia\",\"canada\",\"india\",\"london\",\"canada\",\"india\",\"australia\",\"london\",\"india\"]]\n",
        "# bids = [112, 3333, 2312, 998, 768, 470, 123, 6546, 866, 911]\n",
        "# characteristics is an array of 'n' un-normalized value-arrays of each characteristic.\n",
        "\n",
        "new_characteristics = correctify_data(characteristics)\n",
        "new_characteristics = create_records(np.asarray(new_characteristics, dtype=float))\n",
        "# print(new_characteristics)\n",
        "bids = np.asarray(bids, dtype=float)[:len(new_characteristics)]\n",
        "# print(bids)\n",
        "# clf = SVC()\n",
        "clf = linear_model.LinearRegression()\n",
        "clf.fit(new_characteristics, bids) #confused about this\n",
        "\n",
        "#SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)\n",
        "\n",
        "# test_data =[5, 555, 1200, \"india\"]\n",
        "# print(\"test_data\", test_data)\n",
        "  \n",
        "# test_data= \n",
        "# print(test_data)\n",
        "\n",
        "test_data_new=[]\n",
        "for t in create_records(np.asarray(test_data)):\n",
        "#   print(t)\n",
        "  test_data_new.append(correctify_test_data(characteristics, t))\n",
        "\n",
        "# print(\"corrected test_data\", test_data)\n",
        "print(\"bids should have been: \", total_bids[int(datalength*0.9): datalength])\n",
        "print(\"predictions: \",clf.predict(np.asarray(test_data_new).astype(float)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1314\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-7facc7d9f83f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcreate_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;31m#   print(t)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m   \u001b[0mtest_data_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrectify_test_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcharacteristics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;31m# print(\"corrected test_data\", test_data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-7facc7d9f83f>\u001b[0m in \u001b[0;36mcorrectify_test_data\u001b[0;34m(characteristics, test_data)\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0mnumeric_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcharacteristics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0mnumeric_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcharacteristics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m             \u001b[0mnew_test_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-7facc7d9f83f>\u001b[0m in \u001b[0;36mconvert_to_numeric\u001b[0;34m(x_array, test)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcorrectify_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcharacteristics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/label.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersect1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdiff1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y contains new labels: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: y contains new labels: ['8190']"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "VrfNlFB_2Xpa",
        "colab_type": "code",
        "outputId": "1e8e2282-af8d-4f64-9355-a6bb6b28eb19",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()SelectedColumns"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c0da9f6c-c165-4cb5-8c85-b3860f48ce62\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-c0da9f6c-c165-4cb5-8c85-b3860f48ce62\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving SelectedColumns.csv to SelectedColumns.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SelectedColumns.csv': b'LotArea,LandContour,Neighborhood,BldgType,HouseStyle,Foundation,BsmtQual,Heating,HeatingQC,TotRmsAbvGrd,SalePrice\\r\\n8450,Lvl,CollgCr,1Fam,2Story,PConc,Gd,GasA,Ex,8,208500\\r\\n9600,Lvl,Veenker,1Fam,1Story,CBlock,Gd,GasA,Ex,6,181500\\r\\n11250,Lvl,CollgCr,1Fam,2Story,PConc,Gd,GasA,Ex,6,223500\\r\\n9550,Lvl,Crawfor,1Fam,2Story,BrkTil,TA,GasA,Gd,7,140000\\r\\n14260,Lvl,NoRidge,1Fam,2Story,PConc,Gd,GasA,Ex,9,250000\\r\\n14115,Lvl,Mitchel,1Fam,1.5Fin,Wood,Gd,GasA,Ex,5,143000\\r\\n10084,Lvl,Somerst,1Fam,1Story,PConc,Ex,GasA,Ex,7,307000\\r\\n10382,Lvl,NWAmes,1Fam,2Story,CBlock,Gd,GasA,Ex,7,200000\\r\\n6120,Lvl,OldTown,1Fam,1.5Fin,BrkTil,TA,GasA,Gd,8,129900\\r\\n7420,Lvl,BrkSide,2fmCon,1.5Unf,BrkTil,TA,GasA,Ex,5,118000\\r\\n11200,Lvl,Sawyer,1Fam,1Story,CBlock,TA,GasA,Ex,5,129500\\r\\n11924,Lvl,NridgHt,1Fam,2Story,PConc,Ex,GasA,Ex,11,345000\\r\\n12968,Lvl,Sawyer,1Fam,1Story,CBlock,TA,GasA,TA,4,144000\\r\\n10652,Lvl,CollgCr,1Fam,1Story,PConc,Gd,GasA,Ex,7,279500\\r\\n10920,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,TA,5,157000\\r\\n6120,Lvl,BrkSide,1Fam,1.5Unf,BrkTil,TA,GasA,Ex,5,132000\\r\\n11241,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Ex,5,149000\\r\\n10791,Lvl,Sawyer,Duplex,1Story,Slab,NA,GasA,TA,6,90000\\r\\n13695,Lvl,SawyerW,1Fam,1Story,PConc,TA,GasA,Ex,6,159000\\r\\n7560,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,TA,6,139000\\r\\n14215,Lvl,NridgHt,1Fam,2Story,PConc,Ex,GasA,Ex,9,325300\\r\\n7449,Bnk,IDOTRR,1Fam,1.5Unf,PConc,TA,GasA,Ex,6,139400\\r\\n9742,Lvl,CollgCr,1Fam,1Story,PConc,Gd,GasA,Ex,7,230000\\r\\n4224,Lvl,MeadowV,TwnhsE,1Story,PConc,Gd,GasA,TA,6,129900\\r\\n8246,Lvl,Sawyer,1Fam,1Story,CBlock,TA,GasA,Ex,6,154000\\r\\n14230,Lvl,NridgHt,1Fam,1Story,PConc,Gd,GasA,Ex,7,256300\\r\\n7200,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,TA,5,134800\\r\\n11478,Lvl,NridgHt,1Fam,1Story,PConc,Ex,GasA,Ex,7,306000\\r\\n16321,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,TA,6,207500\\r\\n6324,Lvl,BrkSide,1Fam,1Story,BrkTil,TA,GasA,Fa,4,68500\\r\\n8500,Lvl,IDOTRR,1Fam,2Story,BrkTil,TA,GasA,TA,6,40000\\r\\n8544,Lvl,Sawyer,1Fam,1Story,CBlock,TA,GasA,Gd,6,149350\\r\\n11049,Lvl,CollgCr,1Fam,1Story,PConc,Ex,GasA,Ex,7,179900\\r\\n10552,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Gd,6,165500\\r\\n7313,Lvl,NridgHt,TwnhsE,1Story,PConc,Ex,GasA,Ex,6,277500\\r\\n13418,Lvl,NridgHt,1Fam,2Story,PConc,Ex,GasA,Ex,9,309000\\r\\n10859,Lvl,CollgCr,1Fam,1Story,PConc,Gd,GasA,Ex,6,145000\\r\\n8532,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Gd,5,153000\\r\\n7922,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,TA,5,109000\\r\\n6040,Lvl,Edwards,Duplex,1Story,PConc,NA,GasA,TA,6,82000\\r\\n8658,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Ex,6,160000\\r\\n16905,Lvl,Timber,1Fam,1Story,CBlock,TA,GasA,Gd,5,170000\\r\\n9180,Lvl,SawyerW,1Fam,SFoyer,CBlock,Gd,GasA,Gd,5,144000\\r\\n9200,Lvl,CollgCr,1Fam,1Story,CBlock,Gd,GasA,TA,5,130250\\r\\n7945,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Ex,6,141000\\r\\n7658,Lvl,NridgHt,TwnhsE,1Story,PConc,Ex,GasA,Ex,6,319900\\r\\n12822,Lvl,Mitchel,1Fam,1.5Fin,PConc,Ex,GasA,Ex,6,239686\\r\\n11096,Lvl,Somerst,1Fam,1Story,PConc,Gd,GasA,Ex,7,249700\\r\\n4456,Lvl,OldTown,2fmCon,2Story,BrkTil,TA,GasA,Gd,8,113000\\r\\n7742,Lvl,Sawyer,1Fam,1Story,CBlock,TA,GasA,Ex,6,127000\\r\\n13869,Lvl,Gilbert,1Fam,2Story,PConc,Gd,GasA,Gd,6,177000\\r\\n6240,Lvl,BrkSide,1Fam,1.5Fin,PConc,TA,GasA,TA,6,114500\\r\\n8472,Bnk,IDOTRR,Duplex,1Story,CBlock,Gd,GasA,TA,5,110000\\r\\n50271,Low,Veenker,1Fam,1Story,CBlock,Ex,GasA,Gd,5,385000\\r\\n7134,Bnk,NAmes,1Fam,SLvl,CBlock,TA,GasA,TA,6,130000\\r\\n10175,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Gd,7,180500\\r\\n2645,Lvl,Somerst,Twnhs,2Story,PConc,Gd,GasA,Ex,7,172500\\r\\n11645,Lvl,CollgCr,1Fam,2Story,PConc,Gd,GasA,Ex,7,196500\\r\\n13682,HLS,StoneBr,1Fam,2Story,PConc,Ex,GasA,Ex,10,438780\\r\\n7200,Bnk,CollgCr,1Fam,1Story,CBlock,TA,GasA,Ex,4,124900\\r\\n13072,Lvl,SawyerW,1Fam,1Story,PConc,Gd,GasA,Ex,5,158000\\r\\n7200,Lvl,IDOTRR,1Fam,2.5Unf,BrkTil,TA,GasA,TA,6,101000\\r\\n6442,Lvl,NridgHt,TwnhsE,1Story,PConc,Gd,GasA,Ex,6,202500\\r\\n10300,Bnk,OldTown,1Fam,2Story,BrkTil,TA,GasA,Gd,9,140000\\r\\n9375,Lvl,CollgCr,1Fam,2Story,PConc,Gd,GasA,Ex,8,219500\\r\\n9591,Lvl,NridgHt,1Fam,2Story,PConc,Ex,GasA,Ex,9,317000\\r\\n19900,Lvl,NAmes,1Fam,1Story,CBlock,Gd,GasA,TA,7,180000\\r\\n10665,Lvl,CollgCr,1Fam,1Story,PConc,Gd,GasA,Ex,7,226000\\r\\n4608,Lvl,OldTown,1Fam,1Story,CBlock,TA,GasA,TA,4,80000\\r\\n15593,Lvl,ClearCr,1Fam,1.5Fin,CBlock,TA,GasW,TA,7,225000\\r\\n13651,Lvl,NAmes,1Fam,1Story,CBlock,Gd,GasA,Ex,8,244000\\r\\n7599,Lvl,Mitchel,1Fam,1Story,CBlock,TA,GasA,TA,4,129500\\r\\n10141,Lvl,Gilbert,1Fam,2Story,PConc,Gd,GasA,Gd,7,185000\\r\\n10200,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Gd,6,144900\\r\\n5790,Lvl,OldTown,1Fam,2Story,CBlock,Fa,GasA,Gd,8,107400\\r\\n1596,Lvl,MeadowV,Twnhs,SLvl,CBlock,Gd,GasA,TA,5,91000\\r\\n8475,Lvl,NAmes,1Fam,1Story,CBlock,TA,GasA,Ex,4,135750\\r\\n8635,Lvl,BrkSide,1Fam,1.5Fin,CBlock,TA,GasA,TA,6,127000\\r\\n10778,Lvl,Sawyer,Duplex,1Story,CBlock,TA,GasA,TA,8,136500\\r\\n10440,Lvl,OldTown,1Fam,2Story,PConc,TA,GasA,Gd,5,110000\\r\\n13000,Lvl,NAmes,1Fam,2Story,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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "Us7ulOzN3AXD",
        "colab_type": "code",
        "outputId": "61131e8f-2a6c-4cd3-8dc7-e51bf57d50e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "# from csv import DictReader\n",
        "# with open(\"SelectedColumns.csv\") as f:\n",
        "#     a1 = [row for row in DictReader(f)]\n",
        "\n",
        "# print(a1)\n",
        "# import csv\n",
        "# with open(\"SelectedColumns.csv\", \"rb\") as f:\n",
        "  \n",
        "#     reader = csv.reader(f)\n",
        "#     i = next(reader)\n",
        "\n",
        "#     print(i)\n",
        " \n",
        "import pandas\n",
        "\n",
        "total_data=[]\n",
        "bids=None\n",
        "data = pandas.read_csv(\"SelectedColumns.csv\", header=0)\n",
        "for col in data.columns:\n",
        "  if col!='SalePrice':\n",
        "    total_data.append(eval('list(data.'+col+')'))\n",
        "  else:\n",
        "    bids= eval('list(data.'+col+')')\n",
        "# print(data.columns.tolist())\n",
        "# col_a = list(data.LotArea)\n",
        "# col_b = list(data.b)\n",
        "# col_c = list(data.c)\n",
        "\n",
        "print(total_data)\n",
        "print(bids)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "1fr51oVCHRZU"
      },
      "cell_type": "markdown",
      "source": [
        "## Highlighted Features\n",
        "### Seedbank\n",
        "\n",
        "Looking for Colab notebooks to learn from? Check out [Seedbank](https://tools.google.com/seedbank/), a place to discover interactive machine learning examples."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "9wi5kfGdhK0R"
      },
      "cell_type": "markdown",
      "source": [
        "### TensorFlow execution"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "S9GW-n-oYWIj"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory allows you to execute TensorFlow code in your browser with a single click. The example below adds two matrices.\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "  1. & 1. & 1. \\\\\n",
        "  1. & 1. & 1. \\\\\n",
        "\\end{bmatrix} +\n",
        "\\begin{bmatrix}\n",
        "  1. & 2. & 3. \\\\\n",
        "  4. & 5. & 6. \\\\\n",
        "\\end{bmatrix} =\n",
        "\\begin{bmatrix}\n",
        "  2. & 3. & 4. \\\\\n",
        "  5. & 6. & 7. \\\\\n",
        "\\end{bmatrix}$"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "oYZkU7ZN3CL0",
        "outputId": "9589151d-5ff4-4bca-f05e-8bcba8ffcba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "input1 = tf.ones((2, 3))\n",
        "input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))\n",
        "output = input1 + input2\n",
        "\n",
        "with tf.Session():\n",
        "  result = output.eval()\n",
        "result  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 3., 4.],\n",
              "       [5., 6., 7.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "nwYF0E3Sjiy4"
      },
      "cell_type": "markdown",
      "source": [
        "### GitHub\n",
        "\n",
        "For a full discussion of interactions between Colab and GitHub, see [Using Colab with GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb). As a brief summary:\n",
        "\n",
        "To save a copy of your Colab notebook to Github, select *File → Save a copy to GitHub…*\n",
        "\n",
        "To load a specific notebook from github, append the github path to http://colab.research.google.com/github/.\n",
        "For example to load this notebook in Colab: [https://github.com/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb) use the following Colab URL: [https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb)\n",
        "\n",
        "To open a github notebook in one click, we recommend installing the [Open in Colab Chrome Extension](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo).\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "yv2XIwi5hQ_g"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualization"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rYs5mx2JZkmy"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory includes widely used libraries like [matplotlib](https://matplotlib.org/), simplifying visualization."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "xqrc5C-IaA5J",
        "outputId": "3460cc84-faf8-4d8c-a4e6-96a6809c389a",
        "colab": {
          "height": 360
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(20)\n",
        "y = [x_i + np.random.randn(1) for x_i in x]\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "_ = plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pbHNv4qHMpRqZLSIRSQUtpgoEA/zP99/ive5TGM4RbIFoHk15mm8/sh6nRmaL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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x562d64cf1b10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AN_LRQ9NkOjs"
      },
      "cell_type": "markdown",
      "source": [
        "Want to use a new library?  `pip install` it at the top of the notebook. Then that library can be used anywhere else in the notebook. For recipes to import commonly used libraries, refer to the [importing libraries example notebook](/notebooks/snippets/importing_libraries.ipynb)."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "FlQq0SUepQbd",
        "outputId": "6e404831-3336-4633-b76f-c6313ecdd356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install -q matplotlib-venn\n",
        "\n",
        "from matplotlib_venn import venn2\n",
        "_ = venn2(subsets = (3, 2, 1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HS+lkU9smrZbT34h+JQYAjz7KcxWpZsyyi7ZRA6bi307jyQSmURIdg6qkva4d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\nxIiISFn/H6CFJPx9gio9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f48ff99ff10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "LxZ3dPzYnyNF"
      },
      "cell_type": "markdown",
      "source": [
        "### Forms\n",
        "\n",
        "Forms can be used to parameterize code. See the [forms example notebook](/notebooks/forms.ipynb) for more details."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "FQ_Hx_9tn7uF",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Examples\n",
        "\n",
        "text = 'value' #@param \n",
        "date_input = '2018-03-22' #@param {type:\"date\"}\n",
        "number_slider = 0 #@param {type:\"slider\", min:-1, max:1, step:0.1}\n",
        "dropdown = '1st option' #@param [\"1st option\", \"2nd option\", \"3rd option\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rTX3heEtu0b2"
      },
      "cell_type": "markdown",
      "source": [
        "### Local runtime support\n",
        "\n",
        "Colab  supports connecting to a Jupyter runtime on your local machine. For more information, see our [documentation](https://research.google.com/colaboratory/local-runtimes.html)."
      ]
    }
  ]
}